Geographic Object-Based Image Analysis – Towards a new paradigm
Thomas Blaschke ⇑
, Geoffrey J. Hay, Maggi Kelly, Stefan Lang, Peter Hofmann, Elisabeth Addink,
Raul Queiroz Feitosa, Freek van der Meer, Harald van der Werff, Frieke van Coillie, Dirk Tiede
Department of Geoinformatics – Z_GIS, University of Salzburg, Hellbrunner Str. 34, A-5020 Salzburg, Austria
a r t i c l e i n f o
Article history:
Received 12 July 2012
Received in revised form 10 August 2013
Accepted 30 September 2013
Available online 10 December 2013
Keywords:
GEOBIA
OBIA
GIScience
Remote sensing
Image segmentation
Image classification
a b s t r a c t
The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and
still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on
image segmentation and use GIS-like spatial analysis within classification and feature extraction
approaches. This article investigates these development and its implications and asks whether or not this
is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss
several limitations of prevailing per-pixel methods when applied to high resolution images. Then we
explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded
as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of
objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments
support important methods in remote sensing such as change detection and accuracy assessment. The
ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and GEOBIA are
analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based
on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific
literature we conclude that GEOBIA is a new and evolving paradigm.
Ó 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier
B.V. All rights reserved.
1. Introduction
Aerial photography has a long tradition dating back to Nadar’s
balloon-based images of Paris, France in 1858, while civilian spaceborne
remote sensing (RS) began in 1972 with Landsat-1. This sensor
set the standards and foundation for future multi-spectral
scanner technologies and its corresponding pixel-based image
analysis. Several digital classification methods (e.g., the maximum
likelihood classifier) were soon developed and became the accepted
processing paradigm of such imagery (Strahler et al.,
1986, see also Castilla and Hay, 2008). Since the late 1990s, this
‘‘pixel-centric’’ view or ‘‘per-pixel approach’’ has increasingly been
criticised (Fisher, 1997; Blaschke and Strobl, 2001; Burnett and
Blaschke, 2003). The pixel based approach has been a dominant
paradigm in remote sensing although very few scientific articles
explicitly use the word ‘‘paradigm’’. In fact, compared to other disciplines,
remote sensing has a surprisingly small theoretical base
beyond the underlying physical concepts of electromagnetic radiation
and its interaction with the atmosphere and other targets. It is
repeatedly argued that this focus on the pixel was and still is
understandable as long as the pixel resolutions are relatively
coarse, i.e., that the objects of interest are smaller than, or similar
in size as the spatial resolution (Hay et al., 2001; Blaschke et al.,
2004). Once the spatial resolution is finer than the typical object
of interest (e.g., single trees, forest stands agricultural fields, etc.)
objects are composed of many pixels and a critical question
emerges: ‘‘why are we so focused on the statistical analysis of single
pixels, rather than on the spatial patterns they create?’’ (Blaschke
and Strobl, 2001).
In this article, we discuss the limitations of this ‘per-pixel’ approach
and the rise of a new paradigm which increasingly competes
with, but also complements the prevailing concept. Castilla
and Hay (2008) argue that the fact that pixels do not come isolated
but are knitted into an image full of spatial patterns was left out of
the early ‘per-pixel’ paradigm. Consequently, the full structural
parameters of the image (i.e., colour, tone, texture, pattern, shape,
shadow, context, etc.) could only be exploited manually by human
interpreters.
However, around the year 2000, the first commercial software
appeared specifically for the delineation and analysis of image-objects
(rather than individual pixels) from remotely sensed imagery.
The subsequent area of research was referred to as object-based image
analysis (OBIA) although terms like ‘‘object-oriented’’ and ‘‘object-specific’’
were often used (Hay et al., 1996, 2003; Blaschke
et al., 2004). Image-objects represent ‘meaningful’ entities or scene
components that are distinguishable in an image (e.g., a house, tree
0924-2716/$ - see front matter Ó 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.isprsjprs.2013.09.014
⇑ Corresponding author. Tel.: +43 662 80445225.
E-mail address: thomas.blaschke@sbg.ac.at (T. Blaschke).
ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 180–191
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or vehicle in a 1:3000 scale colour airphoto). Thus, image-objects
are inherently scale-dependent.
OBIA incorporates older segmentation concepts in an initial but
essential step while further bridging spatial concepts applied to
evolving image-objects and radiometric analyses that are earth
surface-centric rather than biological, medical or astronomical
(segmentation is also practiced in these domains). Hay and Castilla
(2008) argue that Geographic space is intrinsic to this analysis, and
as such, should be included in the name of the concept and, consequently,
in the abbreviation: ‘‘Geographic Object-Based Image
Analysis’’ (GEOBIA). Only then it is clear that we refer to a sub-discipline
of Geographic Information Science (GIScience). While this
seems both logical and obvious to Remote Sensing scientists, GIS
specialist and many environmental disciplines, the fact that remote
sensing images ‘model’ or ‘capture’ instances of the Earth’s
surface may not be obvious to scientists from other disciplines
such as Computer Vision, Material Sciences or Biomedical Imaging.
In the remainder of this article, we will use the term ‘‘GEOBIA’’
henceforth.
In the following section, we will discuss the limitations under
some situations of the traditional pixel-based approach. In Section
3, we analyse and discuss indications of a paradigm and discuss
whether GEOBIA fulfils such criteria. In Section 4 we
identify the key concepts of GEOBIA and we conclude that GEOBIA
bridges remote sensing, image analysis and GIS analysis concepts.
2. Remote sensing and image processing concepts and
limitations
The digital analysis of remotely sensed data evolved from concepts
of manual image interpretation. Although developed initially
based on aerial photographs, these protocols are also applicable to
digital satellite imagery. Many digital image analysis methods are
primarily based only on tone or colour, which is represented as a
digital number (i.e., brightness value) in each pixel of the digital
image (for a recent literature overview see Weng, 2009, 2011;
Fonseca et al., 2009; Myint et al., 2011). Along with the advent of
multi-sensor and higher spatial resolution data more research focused
on image-texture as well as contextual information, which describes
the association of neighbouring pixel values and has been
shown to improve image classification results (Marceau et al.,
1990; Hay and Niemann, 1994; 1996).
2.1. H- and L-resolution
In their classic paper Strahler et al. (1986) introduce a conceptual
remote-sensing model comprising three sub-models: (i) the
scene, (ii) the sensor and (iii) the atmosphere model. The scene is
the landscape from which radiance measurements are acquired.
These three sub-models together form the framework in their
study, but for GEOBIA the scene and sensor/image models are particularly
important. The scene model provides a simplification of
the real world. It describes the real-world objects as the analyst
would like to extract them from images in terms relevant to image
processing. Thus, the legend is an important part of the scene model
as it describes thematic characteristics of objects, and roughly
implies the size of objects. Generally, more detailed thematic
descriptions are related to smaller objects. For example, a forested
area contains trees. The sensor model describes the specifics of the
measurements from which the image is built including the number
of spectral bands and their bandwidths. It also defines spatial aspects
like the resolution cell, which specifies the surface area over
which radiance is registered. Strahler et al. also introduced the concepts
of H- and L-resolution, which, as they specifically note, should
not be indicated by descriptors of ‘High’ and ‘Low’ resolution, as
these are commonly applied to specific sensors and their associated
pixel size [e.g. Ikonos (1.0 m PAN) vs. AVHRR (1.0 km)]. Here,
(spatial) resolution refers to the combined spatial aspects of the
scene and the sensor/image models. H-resolution indicates situations
where scene objects are much larger than the resolution cells,
thus several resolution cells may contain radiance data of a single
object. L-resolution represents the opposite situations where scene
objects are much smaller than the resolution cells. While a pixel
contains both H- and L- resolution information, each of which
can be used for image analysis (Hay et al., 2001) GEOBIA is primarily
applied to very high resolution (VHR) images, where imageobjects
are visually composed of many pixels; and where it is
possible to visually validate such image-objects (i.e. H-resolution
case). The use of GEOBIA, however, is not limited to images with
small resolution cells. If the legend of the scene model is generalized,
i.e. a higher hierarchical level of the legend is applied, then
the size of scene objects will increase and an L-resolution situation
may turn into an H-resolution situation.
A common issue with coarse resolution cells is that they combine
spectral properties of heterogeneous land cover. For example,
in the case of a resolution cell of 1 km2
in a forested area, the scene
will contain mostly forest (typically of more than one species), but
probably also open patches, paths and roads, or small fens etc.
Although the spectral properties will be dominated by forest vegetation,
they will not represent ‘pure’ forest. Hence, spectral mixing
increases in images with coarser resolution cells which in turn
leads to confusion during classification. While creating object attributes,
the spectral properties of individual cells are averaged for
the entire object. This reduces classification confusion as averaging
diminishes the (within-object) variance and seems to be appropriate
for classification of coarse resolution images. At present, per
pixel image analysis of coarse spatial resolution images (e.g.,
MODIS, AVHRR) remains the base producer of spatially continuous
land cover information. The production of classified thematic maps
by broadband multi-spectral imagery, however, has evolved due to
the advent of high spatial resolution imagers.
2.2. Advances in image classification
Throughout the last 15–20 years, advanced classification approaches,
such as artificial neural networks, fuzzy logic/fuzzy-sets,
and expert systems, have become widely applied for image classification.
Weng (2009) provides a valuable list of the major advanced
classification approaches that have appeared in recent
literature, dividing the approaches into the following major categories
with subsequent sub-categories: per-pixel (17 categories),
sub-pixel (7 categories), per-field (6 categories), contextual based approaches
(13 categories), knowledge based (6 categories), and combinational
approaches of multiple classifiers (14 categories). Weng
(2009) includes GEOBIA within the category ‘Per-field classification’
(see next paragraph), which may be used to explain the role of segmentation
in GEOBIA: segmentation is only one possible means to
delineate objects of interest. If they are derived otherwise, e.g. imported
from a GIS database, we may more explicitly call the subsequent
classification process a per-field classification. Interestingly,
GEOBIA methods are only one of the 63 specified by Weng,
although its number of literature references per category (from
international journals between 2003 and 2004) is the highest
overall.
In an effort to improve pixel based classifications by exploiting
scene characteristics other than ‘colour’ – such as tone, shape pattern,
context etc., the most widespread approaches incorporate
information on image-texture and pattern, based on moving window
or kernel methods, the most common being the Grey Level
Co-occurrence Method (GLCM) (Haralick et al., 1973; Marceau
et al., 1990). Since the late 1980s, geostatistical approaches have
T. Blaschke et al. / ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 180–191 181
also been used to exploit the information content of remote sensing
imagery, in particular variogram-based approaches. The variogram
is a measure of spatial dependence, that has been used to
quantify image structure linking remote sensing and geostatistical
theory (Curran, 1988). It has also been proposed as an alternative
measure of image-texture as it relates to image variance and spatial
association (Hay et al., 1996). For the sake of completeness we
note that we leave out sub-pixel classification in this brief discussion
since we concentrate on H-Res situations.
Per-field classification approaches have shown improved results
in some older studies (e.g. Lobo et al., 1996). In fact, the
widely-known ECHO algorithm (Kettig and Landgrebe, 1976) is a
two-step approach using the results from an initial single pass region
growing segmentation as outlines for a subsequent ‘per-field
classification’. Results of per-field classifications are often easier
to interpret than those of a per-pixel classification (Blaschke
et al., 2004). The results of the latter often appear speckled even
if post-classification smoothing is applied. ‘Field’’ or ‘parcel’ refers
to homogenous patches of land (agricultural fields, gardens, urban
structures or roads) which already exist and are superimposed on
the image.
2.3. Limitations of the ‘per-pixel’ approach
Most of the methods for image processing developed since the
early 1970s are based on classifications of individual pixels utilizing
the concept of a multi-dimensional feature space. In Section 2.2
we have shown that a range of sophisticated and well established
techniques have been developed that classify L-resolution images
by pixels. However, it is increasingly recognized that the current
demand from the remote sensing community and their clients –
in respect to ever faster and more accurate classification results –
is not fully met due to different characteristics in high resolution
imagery and varying user needs (see e.g. Wang et al., 2009). New
H-resolution sensors significantly increase the within-class spectral
variability and, therefore, decrease the potential accuracy of a
purely pixel-based approach to classification. Hay et al. (1996) referrs
to this as ‘The H-Resolution problem’.
2.4. Challenge 1: objects
Objects are never exclusively a construct used to discuss environments
as such; instead they are part of discourses that shape
our thinking about space, time and relations (Massey, 1999). The
key point is that pixels may not be seen relationally. However, objects
are both the product of the attention of a thoughtful observer,
and the resulting matter and processes. Objects may also be the
product of the representational devices deployed (Ahlqvist et al.,
2005) – that is, the emergent scene structures/patterns resulting
from specific processes ‘captured’ at a particular scale (spatial,
spectral, temporal, radiometric). Mixed pixels may serve as an
illustration here: a pixel whose digital number represents the average
of several spectral classes within the area that it covers on the
ground, each emitted or reflected by a different type of material are
likely to be misclassified and their existence is highly influenced by
the resulting variations caused by the data acquisition process. In
contrast, GEOBIA is focused on research into the conceptual modelling
and representation of spatially referenced imagery. By bridging
GIS, remote sensing and image processing it integrates
numerous ‘spatial perspectives’. For example, it relies on the concepts
of space, spatial features and geographical phenomena, and
it provides a spatial view into various kinds of physical and abstract
information objects including natural and anthropogenic
landforms/landcover and the cultures that may have formed them,
e.g., Quebec’s agricultural long-lots, rice terraces in China, or favela’s
in Brazil.
2.5. Challenge 2: shape
Identification of objects by human vision is based on a combination
of factors like shape, size, pattern, tone, texture, shadows and
association (Olson, 1960). Geometry, the combination of shape and
size, together with tone are major factors. Shape refers to general
form or outline of individual objects, while tone indicates the spectral
properties of an individual band (Lillesand et al., 2008). With
per-pixel classification, spectral properties are by far the most
important for identifying objects; however, by applying filters,
some local variance in pixel values can also be included, though
spectral information is dominant. When an object class has a unique
spectral ‘signature’, classification is relatively ‘trivial’. However,
when an object class shares spectral signatures with other
classes, classification often proves difficult. We note that an implicit
shape is seldom if ever evaluated or defined pre-classification –
except in the case of feature detection (and template matching).
GEOBIA offers possibilities for situations where spectral properties
are not unique, but where shape or neighbourhood relations
are distinct. For example, river meanders will have the spectral
properties of water when they are still active, but once they are
abandoned a range of possibilities exists (Addink and Kleinhans,
2008). They can remain water filled, they can be filled in by sediment,
they can be overgrown by vegetation, or a combination of
these three land cover situations might occur (Fig. 1). These
land-cover types are not unique to meanders, thus prohibiting
their identification by spectral properties alone. However, the
shape of the meander will remain unchanged, thus offering a unique
property that can be used to identify meanders independent
from their land cover appearance.
The size of the meanders depends on the discharge and may
therefore show considerable variation. By creating object sets by
different spectral heterogeneity thresholds and adapting the shape
criteria, meanders with different sizes and different spectral properties
could be identified. Although geometry will often not be distinct
by itself, in many situations it will be a valuable factor in the
identification of objects.
Fig. 1. Subsets of Landsat TM scenes from Alaska (left) and Bangladesh (right). The
left water filled channel intermingles with an old sediment-filled channel. The right
portion of the water filled channel is overgrown by vegetation.
182 T. Blaschke et al. / ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 180–191
2.6. Challenge 3: texture
In natural or near-natural environments, transitions may by
fuzzy or gradient-like. This causes problems for a classification process
that necessitates crisp decisions. A gradient operator applied
to a raw intensity image will not only respond to intensity boundaries
but also the intensity variation due to object texture resulting
in a significant number of false positives. Image-texture refers to
particular frequencies of change in tones and their resulting spatial
arrangements (Hay and Niemann, 1994). For the human interpretation
of images the visual impression of smoothness or roughness
of an area is an important cue. For example, water bodies typically
are finely (smoothly) textured; grass may be regarded as a medium
texture and brush as rough (although there are always exceptions).
As these simplified examples reveal, image-texture involves spatial
context; thus it is unable to exist at a single pixel or point (Hay
et al., 1996). In the per-pixel approach, information on texture is
typically derived using a moving window or kernel method of a
fixed size, shape and (limited) orientation(s). A more powerful
form of image-texture is to build on the spatial interaction of
neighbouring image-objects (Hay and Niemann, 1994; Powers
et al., 2012).
2.7. Challenge 4: context and pattern
Addressing real entities such as trees may require to mining
their context and pattern across scales. One convincing example
where ecological information could be addressed through multiscale
object building is provided by de Chant and Kelly (2009).
For a new forest disease in California (USA) called sudden oak death,
these authors found key insights into disease ecology and impact
by considering individual trees as objects in a remote sensing classification
process. They confirm the importance of non-oak hosts in
spreading the disease by examining the spatial patterning of individual
oaks and their neighbours in space and time (Kelly et al.,
2008; Liu et al., 2006); and that these characteristics are relevant
at multiple scales, and displayed hierarchies (Liu et al., 2007). Even
sophisticated adaptive kernel-based methods would fail. Additionally,
these kinds of multi-scaled patterns can be used to construct
rules for classifying image-objects and refining GEOBIA classification
results (Liu et al., 2008), and in concert with other key concepts
(e.g. shape, texture, etc.) convey important agency to the
resulting objects (see Fig. 2). Such ‘rules’ may also be used in Cellular
Automata and Agent based modelling (Marceau and Benenson,
2011).
2.8. Challenge 5: semantics and knowledge integration
Basic entities composed of pixels are limited to be used as constituents
for semantic information. Pixels are limited in supplementing
our implicit knowledge with explicit knowledge
obtained from formal learning situations (e.g. spectral behaviour
of stressed vegetation). From an Artificial Intelligence (AI) perspective
knowledge can be distinguished as procedural and structural
knowledge. Procedural knowledge is concerned with specific computational
functions and can be represented by a set of rules. Structural
knowledge – understood here as declarative knowledge –
implies how concepts of a domain are interrelated: in our case this
means, the relationship between image-objects and ‘real world’
geographical features (Castilla and Hay, 2008). Structure is characterised
by high semantic content and therefore is more difficult to
tackle. Tiede et al. (2010a), Tiede et al. (2010b) applied semantic
modelling to deliver functional spatial units (so called biotope
complexes) for regional planning tasks. The categories addressed
(e.g. mixed arable land, consisting of different types of agricultural
fields in a specific composition) represent composite objects
consisting of homogenous building blocks (elementary units).
The target categories are modelled by their specific internal
arrangements. This internal arrangement is a structural, not a statistical
(i.e. pattern) feature and requires explicit spatial relationships
to be addressed. To underline this fact, the term ‘class
modelling’ is used by Lang (2008).
Structural knowledge can be organised into knowledge organizing
systems, realised by graphic notations such as semantic networks
(Liedtke et al., 1997; Sowa, 1999), and by more
mathematical theories like the formal concept analysis (FCA, Ganter
and Wille, 1996). Within image analysis, semantic nets and
frames (Sowa, 1999) offer a formal framework for semantic knowledge
representation using an inheritance concept (is part of, is
more specific than, is instance of) (Lang, 2005; 2008) – which is
also a foundation of Object-Oriented (OO) programming.
3. Is GEOBIA a paradigm?
3.1. Kuhn’s paradigm concept
In 1962 Thomas Kuhn published ‘‘Structure of Scientific Revolutions’’,
a highly influential book that described the process of
intellectual revolution. The key concept – if extremely condensed
and simplified – is that common practice may be regarded as
normal science whereas new concepts when clearly contradicting
established thoughts may be called revolutionary science.
Kuhn’s main hypothesis is that scientific development is not
smooth and linear. Instead it is episodic – that is, different kinds
of science occur at different times. The most significant episodes
in the development of a science are normal science and revolutionary
science. It is not a cumulative process, since revolutionary
science typically discards some of the achievements of earlier
scientists. Typically, individual scientists seek to solve the puzzles
they happen to be faced with and they are not interested
in a fixed scientific method per se. Instead scientists make discoveries
based on their training with exemplary solutions to past
puzzles, which Kuhn calls paradigms. There is some vagueness in
the definition of a paradigm (see Kuhn, 1962, p. 181ff). Nonetheless,
Kuhn provides a widely accepted framework for describing
Fig. 2. False-colour digital image of a forest stand with sudden oak death in CA
showing selected objects representing dead trees (grey) and associated hosts
(magenta), and illustrating three common image spatial resolutions: 30 m, 4 m and
1 m. (For interpretation of the references to colour in this figure legend, the reader
is referred to the web version of this article.).
T. Blaschke et al. / ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 180–191 183
how change, and all that implies, occurs in science. A paradigm
is ‘‘what the members of a scientific community share’’ (Kuhn
1962, p. 176). This comprises not only the laws and results of
this scientific community but the methodologies, the aims, the
conventions, the research questions and their unsolved problems.
Research questions are expected to be answered within
the constraints of the paradigm.
Who is this scientific community? It includes the scientists, students,
observers and philosophers who share and strive to maintain
the paradigm. The community changes as scientists are trained and
die; the paradigm changes as new observations are made. However
the change is such that the paradigm will be strengthened since it is
used as an exemplar. Thus, the paradigm notion refers to a shared
set of assumptions, values and concepts within a community (Pollack,
2007). Additionally, according to Kuhn, a scientific revolution
will revise some of the previous paradigm but not necessarily all of
it. To be accepted, a proposed new paradigm must retain some
achievements of its predecessor; as well as scientists trained in
the old paradigm.
3.2. What characterizes a paradigm?
Social scientists have adopted Kuhn’s concept of a paradigm
shift to denote a change in how a scientific community organises
and understands reality. A dominant paradigm refers to the values,
or system of thought in a society that are most standard and
widely held at a given time. Furthermore, dominant paradigms
are typically shaped by the community’s cultural background and
their historical development. Some authors have commented in
previous writings that GEOBIA is a paradigm (e.g. Hay and Castilla,
2008). However, it should be stated explicitly here that we do not
claim that the per-pixel remote sensing approach is wrong, merely
that we now have a different understanding of the world. For over
a decade this has been supported by numerous journal articles that
have shown that object-based classification results (especially of
H-resolution imagery) are consistently better than those based
on traditional pixel-based approaches (e.g., Shackelford and Davis,
2003; Yu et al, 2006; Blaschke, 2010; Myint et al., 2011; Whiteside
et al., 2011).
We also note that the concept and act of revolution that Kuhn
describes was necessary (especially in earlier times) for change(s)
to take place, as the scientific establishment was typically very
conservative, and its power-base, which was often associated with
important social, political and financial structures, was held in the
hands of a select few influential individuals – whom seldom relinquished
it without a fight (a.k.a revolution). However today, ubiquitous
global media and communication technologies increasingly
place the power of change in the hands of ‘the people’ rather than a
select few. And though ‘disruptive technologies do exist, and ideas
can quickly become ‘viral’, the vast majority of today’s scientific
change follows a more evolutionary path, rather than that of a revolution
(Hay and Castilla, 2008).
In fact, Kuhn also claims that the world has changed as he noted
‘‘...we may want to say that after a revolution, scientists are
responding to a different world’’ (Kuhn 1962, p. 111). And, while
Kuhn states that the new paradigm replaces the old one, dozens
of scientists from different disciplines have more recently argued
that some disciplines are ‘‘multi-paradigmatic’’ (e.g. Lukka, 2010)
and that the diversity of world views is the key to interpretation
and understanding of it.
Based on a synthesis of these ideas, combined with our own
experiences, we suggest the following general conditions support
the idea of a (new) paradigm becoming more widespread or even
accepted:
 The absolute number of (new paradigm) publications
increases along with the acceptance rate in renowned
journals.
 Conferences devoted to discussing ideas and methods central
to the (new) paradigm.
 The development of professional organizations that give
legitimacy to the (new) paradigm.
 Dynamic leaders who introduce and purport the (new) paradigm
through papers, presentations, and more recently
through blogs, tweets, Wiki’, etc.
 Books and special issues of journals on the new approach/
paradigm.
 Scholars who promulgate the paradigm’s ideas by teaching
it to students and professionals.
 The creation of related free and open source software/tools
and online communities to support the use, development,
and promotion of these new ideas and methods.
 The development of commercial software and promotion of
industry supported communities and programs.
 The creation and implementation of new (related) standards,
and their (continued) evolution.
 Government agencies who give credence to the paradigm,
informally or formally.
 Increased media coverage.
Although this list is not exhaustive and some parameters are
not easily measurable, we will provide support in the following
sub-section that GEOBIA can be regarded as a paradigm.
3.3. Facts which support the GEOBIA paradigm hypothesis
Synthesizing existing definitions (Hay and Castilla, 2008; Hay
and Blaschke, 2010) we may state that GEOBIA is a ‘recent’ approach
(including theory, methods, and tools) to partition remote
sensing imagery into meaningful image-objects, and assess their
characteristics through scale. Its primary objective is the generation
of geographic information (in GIS-ready format) from which
new spatial knowledge or ‘‘geo-intelligence’’ (Hay and Castilla,
2008) can be obtained. Here, geo-intelligence is defined as geospatial
content in context (Hay and Blaschke, 2010). GEOBIA is not limited
to the remote sensing community but also embraces GIS,
landscape ecology and GIScience concepts and principles, among
others. The following points support the notion that GEOBIA is a
new paradigm.
Blaschke (2010) previously diagnosed a significant body of relevant
literature in this field and noted a particularly fast increase in
peer-reviewed literature. Similarly, we undertook a brief literature
survey using Google Scholar (GS), WebofKnowledge (WoK) and
SCOPUS (Elsevier). Results show that not only is the number of
articles increasing, but that the rate of growth is dramatically
accelerating. Blaschke (2010) performed his search in April 2009
and identified 145 journal papers relevant to GEOBIA. Since more
and more GEOBIA methods are integrated into application papers
it is difficult to provide an exact number. However, based on a literature
analysis using Web of Knowledge and Scopus by using various
spelling alternatives we estimate the number of relevant
journal articles to be over 600 (September 2013) which means that
they have more than quadrupled over the last four and a halfyears
(see Table 1).
Several international journals dedicated special issues to GEOBIA
including GIS – Zeitschrift für Geoinformationssysteme (2001),
Photogrammetric Engineering & Remote Sensing (2010; 2014), Journal
of Spatial Science (2010), Remote Sensing (2011; 2014), Journal
of Applied Earth Observation and Geoinformation (2012), IEEE Geoscience
and Remote Sensing Letters (2012).
184 T. Blaschke et al. / ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 180–191
Several workshops and four bi-annual international conferences
were held including (i) the OBIA International conference, Salzburg,
July 2006, Austria; (ii) Object-based Geographic Information Extraction,
June 2007, Berkeley, USA; (iii) GEOBIA 2008: Pixels, Objects,
Intelligence: Geographic Object-based Image Analysis for the 21st Century,
Calgary, Canada; (iv) Object-based Landscape Analysis, 2009,
Nottingham, UK; (v) GEOBIA 2010, Ghent, Belgium, (vi) GEOBIA
2012, May 2012, Rio de Janeiro, and (vii) GEOBIA 2014, Thessaloniki,
Greece.
Several books are dedicated to OBIA and GEOBIA topics, most
notably the Springer book edited by Blaschke et al. (2008). According
to information provided by Springer (personal communication:
04 June 2012) the total number of chapter downloads is 20033.
Other indications mentioned above include respective university
classes, job announcements or even dedicated professor positions.
We could not carry out an in-depth survey but we are aware
of individual evidence at Universities in Europe, the US, Canada,
Brazil, Australia and China. A rough internet-based search finds
more than 30 finished or on-going PhD projects which have the
terms OBIA or GEOBIA included in the title, keywords or abstract.
Last but not least we may mention that a significant amount of
public institutions and agencies are using GEOBIA software at professional
and operational levels. This ranges from Nature Conservation
agencies to the Military which creates a high demand in
training and education beyond simple software competency.
4. Geographic Object-based Image Analysis – key concepts
4.1. Human interpretation and perception as guiding principles for
GEOBIA
In an effort to better understand and develop a more explicit
GEOBIA framework, Hay and Castilla (2008) provided a number
of tenants or fundamental guiding principles. They described GEOBIA
as exhibiting the following core capabilities: (i) data are earth
(Geo) centric, (ii) its analytical methods are multi-source capable,
(iii) geo-object-based delineation is a pre-requisite, (iv) its methods
are contextual, allowing for ‘surrounding’ information and
attributes, and (v) it is highly customizable or adaptive allowing
for the inclusion of human semantics and hierarchical networks.
Lang (2008) also describes a selection of GEOBIA guiding principle
for complex scene content so that the imaged reality is best described,
and the maximum (respective) content is understood,
extracted and conveyed to users (including researchers). For details
see Lang (2008, pp. 14–16).
Many consider that the ultimate benchmark of GEOBIA is the
generation of results equalling or better than human perception,
which is far from trivial to numerically quantify and emulate. Human
perception is a complex matter of filtering relevant signals
from noise (Lang, 2008), a selective processing of detailed information
and, finally, experience. Enormous advances have been made
in computer vision but the potential of human vision remains to
be achieved. While biophysical principles like retinal structure
and functioning and singular processes such as the cerebral reaction
are analytically known, we still lack the bigger ‘picture’ of human
perception as a whole (Lang, 2005). Cognitive psychology tells
us about mechanisms we use in perceiving patterns and spatial
arrangements, and Marr (1982) provides a conceptual framework
of a three-levelled structure of visual information processing (Eysenck
and Keane, 1995).
Lang (2005) elaborates on the relation between image perception
and image interpretation and refers to the original literature
in neuro-psychology for concepts such as ‘experience’ in the context
of images and suggests that more than one model is used to
construct meaning from an image (Lang et al., 2004). Image interpretation,
when dealing with an unfamiliar perspective and scale,
requires ‘multi-object recognition’ in a rather abstracted mode,
and the interpreter needs to understand the whole scene. According
to Gibson (1979), values and meanings of objects are attributed
via (object) ‘affordance’ (Lang et al., 2009). The skilled visual interpreter
may recognise some features instantly and others by matching
the visual impression against experience or examples listed in
an interpretation key. All these concepts – and many which cannot
be discussed here – are difficult to be used in per pixel analyses.
However, they can be addressed more appropriately through GEOBIA
concepts, of which several key components will be discussed in
the following section.
Blaschke et al. (2004) elucidate the relationship between pixels
and image-objects. A pixel is normally the smallest entity of RS
imagery. A pixel’s dimensions are determined by the sensor and
scene geometric models. Image-objects as defined by Hay et al.
(2001) are basic entities, located within an image that are perceptually
generated from High-resolution pixel groups, where each
pixel group is composed of similar data values, and possesses an
intrinsic size, shape, and geographic relationship with the realworld
scene component it models. Possible strategies to model
spatial relationships and dependencies present in RS imagery are
Table 1
Citations of highly cited GEOBIA papers in Web of Knowledge (WoK), SCOPUS and Google Scholar (GS) for September 2013 compared to the respective figures – if available – from
Blaschke (2010) based on a survey conducted in April 2009.
2013 2009
Authors WoK SCOPUS GS WoK GS
Benz et al. (2004) 570 655 1139 150 220
Blaschke (2010) 217 338 555 – –
Blaschke et al. (2000) – – 250 – 76
Blaschke and Strobl (2001) – 172 383 – –
Burnett and Blaschke (2003) 179 182 335 63 101
Desclée et al. (2006) 106 125 164 – –
Câmara et al. (1996) 168 182 857 49 331
Yu et al. (2006) 159 163 273 – –
Shackelford and Davis (2003) 100 126 201 – –
Hay et al. (2001) 88 87 162 – –
Hay et al. (2003) 104 134 229 41 71
Hay et al. (2005) 102 117 160 – –
Laliberte et al. (2004) 155 162 255 – –
Walter (2004) 143 185 292 – –
For comparison: top image processing articles beyond remote sensing applications
Haralick and Shapiro (1985) 889 812 2134 720 1104
Pal and Pal (1993) 1120 1362 2679 777 1187
T. Blaschke et al. / ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 180–191 185
centred on image segmentation and ‘objectification’ whereby the
latter is understood as the integrated spatial and thematic object
definition. The following key concepts, while not exhaustive represent
important building blocks of GEOBIA methodology.
4.2. Image segmentation: not an end in itself
In GEOBIA, image segmentation is not an end in itself. Segmentation
is the partitioning of an array of measurements on the basis
of homogeneity. It divides an image – or any raster or point data –
into spatially continuous, disjoint and homogeneous regions referred
to as ‘segments’. In feature extraction, it can be regarded as
an end in itself. In GEOBIA, it is one step in a processing chain to
ultimately derive ‘meaningful objects’. Blaschke et al. (2004) following
the basic methodology of Burnett and Blaschke (2003) describe
this process when referring to the ‘near-decomposability’ of
natural systems as laid out by Koestler (1967). Simply speaking,
the resulting internal heterogeneity of a segment under consideration
shall be less than the heterogeneity when it is taken in conjunction
with its neighbours.
Image segmentation was well established throughout the late
1970s and the 1980s (see Haralick and Shapiro, 1985), with numerous
segmentation algorithms available (Pal and Pal, 1993). Traditional
segmentation methods are commonly divided into three
main approaches: (i) pixel-, (ii) edge and (iii) region-based segmentation
methods. Though available to the computer science
community, image segmentation was seldom used (exceptions
were already acknowledged, see e.g. Kettig and Landgrebe, 1976;
Câmara et al., 1996) for the classification of earth observation data,
as most algorithms were developed either for pattern analysis, the
delineation of discontinuities on materials or artificial surfaces, or
quality control of products (Blaschke et al., 2004).
While GEOBIA may be believed to be critically dependent on the
appropriate choice of a segmentation technique there are very recent
developments which are decreasingly dependent on the initial
segmentation. In several of these approaches (see e.g. Baatz and
Schäpe, 2000; Lang et al., 2010; Tiede et al., 2010a; Tiede et al.,
2010b, 2011, 2012) segmentations are used very flexibly in initial
stages and are also tailored in a later stage for specific classes or regions
in the image when the classification process requires this. A
group of researchers from Brazil recently developed segmentation
software in which different shape features may be used to express
heterogeneity within the region growing process (Feitosa et al.,
2011). In their experiments they ‘‘optimize’’ the segmentation
parameter values for each set of shape features being considered.
Their results showed that the segmentation accuracy may be considerably
improved when shape features are used in the formulation
of a heterogeneity criterion – which is essential for a
‘meaningful’ segmentation.
4.3. Putting pixels into context
A key issue when segmenting earth observation data is the fundamental
difference between a scene and an image. An image is a
collection of measurements (at a specific time and location) from a
sensor that are arrayed in a systematic fashion. Thus a scene-object
(for all intents and purposes) is a (fiat) real world object, while an
image is a collection of spatially arranged samples that model the
scene. Essentially, it is the sensor’s ‘view’ of the scene. Consequently,
while image segmentation groups pixels that are alike in
terms of registered values, it is possible and highly probable that
a one to one relationship may not exist between scene objects
and the image objects or the underlying segments that model them
(see Fig. 3 and explanation below).
Another issue is that images are only snapshots, and their size
and shape are dependent upon the sensor type and spatial sampling
of the remotely sensed image from which they are derived.
Thus, segments are not by definition ‘meaningful’. For example,
through segmentation, two adjacent forest stands could end up
in a single (image-) object even though they are managed differently
or are owned by different proprietors.
Fig. 3 exemplifies a complex workflow from ‘segments’ to image-objects.
The latter are ‘meaningful’ groupings with regard to
a particular context or aim. Imagine the existence of a real boundary
between two forest stands, such as a creek. This boundary
would also need to be spatially and spectrally distinct in relation
to the spatial resolution of the image in order for a segmentation
process to generate a new object. Conversely, (at a finer spatial resolution)
a single forest stand can feature considerable internal variability
(e.g., due to the health condition of the constituent trees)
causing the segmentation process to over-segment the scene and
create multiple objects within a single stand.
A final thought pertaining to the configuration at which imageobjects
manifest themselves. Due to the typical pixel-wise representation
of earth observation data, segmentation of image data always
yields ‘pixelated’ object shapes. This could cause problems
when comparing these image-objects with other spatial informaFig.
3. Idealized GEOBIA workflow that illustrates the iterative nature of the object
building and classification process which incorporates GIScience concepts.
Fig. 4. Pixels and image-objects as information carriers: constant size, constant
shape and implicit location vs. unique area/outline information derivatives and
statistical descriptors of the interior. For the sake of simplicity, the temporal
dimension is left out here.
186 T. Blaschke et al. / ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 180–191
tion like e.g. cartographic data. Segmentation of earth observation
data is very often followed by an ‘objectification’ (i.e. classification)
step eventually leading towards meaningful object definitions.
Still, a core concept is that objects are the main information carrier
(see Fig. 4).
Among open challenges in GEOBIA are methods describing multi-temporal
behaviour (deChant and Kelly, 2009; Chen et al., 2012).
Two recent papers describe additional approaches for cascaded
multi-temporal classifications pursued at the Catholic University
Rio de Janeiro (Feitosa et al., 2011; Leite et al., 2011).
4.4. GEOBIA and the Object-Oriented (OO) data model
The subject of GEOBIA is related to concepts of object-oriented
software and to object handling in the GIS world; for additional
information the reader is referred to an OO review paper by Bian
(2007). OO concepts and methods have been successfully applied
to many different problem domains, and there is great opportunity
to adapt and integrate many of its beneficial components to GEOBIA
(Hay and Castilla, 2008), as the majority of early GEOBIA research
was conducted without OO software, tools or languages.
This integration not only includes OO programming, but all the
corpus of methods and techniques customarily used in biomedical
imaging and computer vision (among others) that remain mostly
unknown to the majority of the remote sensing community.
Unlike the geo-relational data model, which separates spatial
and attribute data and links them by using a common identifier,
the object-oriented data model views the real world as a set of
individual objects that may have spatial and non-spatial interrelationships
among each other. Thus, an object has a set of properties
and can perform operations on requests (Worboys, 1995).
Baatz et al. (2008) argue to call more complex GEOBIA workflows
‘‘object-oriented,’’ due to the fact that the objects are not
only used as information carriers but are modelled with the continuous
extraction and accumulation of expert knowledge. That is, by
incorporating expert knowledge from the application and image
processing domain the initial segmentation results are optimized
step by step through dedicated processing steps. The aim of this
optimisation process is to generate image-objects that fulfil the
major criteria of the intended entities in the image domain. In
many cases, an a priori defined ontology of the image-objects to detect
is used as a tool to model real-world objects (Clouard et al.,
2010; Hofmann et al., 2008; Hofmann et al., 2006). In order to describe
natural variability, many models need to be capable of
expressing their vagueness (e.g. by fuzzy rules) and to be adaptable
according to unforeseeable imaging situations (Benz et al., 2004;
Hofmann et al., 2011). For instance, a meadow can be spectrally
more or less homogeneous but at the time of the image acquisition
some agricultural machines could be left there. Rules which refer
the larger entity of a meadow consisting of thousands of image pixels
should allow for small islands of contrast within. This would
typically be handled through object-sub-object relationships (see
Fig. 5).
4.5. GIS-like functionality for classification
When classifying segments – rather than pixels – size, shape,
relative/absolute location, boundary conditions and topological
relationships can be used within the classification process in addition
to their associated spectral information (as done by human
photo interpreters). In fact, some GEOBIA researchers claim that
this is a key to the popularity and utility of this approach. There
is increasing awareness that object-based methods make better
use of – often neglected – spatial information implicit within RS
images, which ultimately allows for a tightly coupled or even full
integration with both vector and raster based GIS. In fact, when
studying the early GEOBIA literature it may be concluded that
many applications were driven by the demand for classifications
which incorporate structural and functional aspects.
4.6. Multi-scale and hierarchies
A very important concept to distinguish GEOBIA from per-pixel
approaches is the ability to address a multiplicity of scales within
one image and across several images. Since its inception, the discipline
of Ecology has considered the notion of scale domains and
scales of variability of different ecological factors, such as plant
morphology and soil nutrients (Greig-Smith, 1979). In fact, this
concept is used to facilitate the search for underlying patterns
and mechanisms and it may be claimed that the concurrence of
scales (often achieved through different levels of segmentation)
may be a seen as a way to model relatively continuous phenomena
(Allen and Starr, 1982) – though Bian (2007) notes the challenges
of delineating the edge(s) of such phenomena. One may critically
note that GEOBIA methods face difficulties in environmental gradients
where parameters gradually, but continuously change. However,
there are many examples in nature where the effects of
‘processes’ are not truly continuous, and as such, GEOBIA may address
a more or less seamless transition between two stages
through super-object/sub-object relations. If a transition was really
continuous then the field concept (Cova and Goodchild, 2002) may
be an appropriate conceptual metaphor to qualify it, though we
have not seen it implemented in existing software. The term ‘‘field’’
here is completely different from the ‘‘per-field’’ classification concept
mentioned earlier. The latter refers to fields in a sense of parcels.
As such, the field and object-based approaches to spatial data
modelling are not mutually exclusive (Worboys 1995, p. 177). In
fact, the concept of Cova and Goodchild (2002) is a hybrid concept
of object fields in which every point in geographic space is mapped
not to a value but to an entire discrete object.
Burnett and Blaschke (2003) developed a five step methodology
which they called ‘‘multi-scale segmentation/object relationship
modelling’’ (MSS/ORM). Multi-scale segmentation has often been
linked with hierarchy theory (Hay et al., 2001; Burnett and Blaschke,
2003; Lang, 2008). This association seems obvious as both
Fig. 5. Hierarchy of image objects. Objects have (topological) neighbourhood
relationships and have hierarchical relationships, such as ‘‘is-part-of’’ or ‘‘consistsof’’.
Respectively they can be (nearly) decomposed.
T. Blaschke et al. / ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 180–191 187
hierarchy theory and multi-scale segmentation deal with hierarchical
organization. As a prerequisite, hierarchy theory proposes
the (near-) decomposability of complex systems (Simon, 1973)
resulting in ‘‘holons’’ (Koestler, 1967) as hierarchically organized,
and multiscale ‘whole-parts’. But as imagery is just a (flat and spectral)
representation of such systems, Lang (2008) introduced the
term ‘‘geons’’ as proxies for holons. He suggests starting from
delineating image regions and then reaching towards image-objects
(geons) while applying segmentation and classification routines
in a cyclic manner. At first glance, hierarchical
segmentation produces regions of increasing average size which
need to be linked to organisational levels (Fig. 6, also cf. Fig. 5).
The quests for ‘‘the right’’ segmentation level and for ‘‘significant’’
scales has led to dozens of empirical investigations and the development
of numerous statistical methods (Hay et al., 2001; Dra˘gutß
et al., 2010).
4.7. Objects, ontologies and semantics
To translate spectral characteristics of image objects to realworld
features, GEOBIA uses semantics based on descriptive
assessment and knowledge, this means, it incorporates ‘‘the wisdom
of the user’’. When studying today’s plethora of literature
we may reason that access to information content by users is a
key success factor at several levels, both within one data set and between
data sets. Commercial data providers and agencies need
effective interfaces for image content so that organizations can
maximize productivity when working with geospatial data. Users
need access to a trusted, up-to-date source of (multiscale) geospatial
data that is easy and flexible to use [and which includes a ‘custody-chain’
of supporting metadata (Hay and Castilla, 2008)].
However, the diversity of users, from government agency experts
to ordinary citizens, represents a significant challenge for effective
information access and dissemination. Indeed, there is no ‘‘one size
fits all’’ solution; however, this situation can exactly be the
strength of GEOBIA.
Increasingly often, a distinct land-cover class may need to be regarded
as a user-driven set of conditions. Such a ‘user-centred cover-class’
may not necessarily be restricted to extractable features,
such as single trees – when classifying orchards. Such demand calls
for recognized and well defined ontologies in order to avoid
stand-alone and black-box solutions (see next sub-section). GEOBIA
methods allow for ‘putting groups of pixels into context’ (see
Section 4.3). Lang et al. (2009) go one step further and describe
conditioned information as the result of a process to fulfil user demands.
Geons (Lang, 2008; Lang et al., 2009) are the building
blocks of this process of information conditioning, being flexible
spatial units, providing a policy-oriented, scaled representation of
administered space, but not confined by administrative units (Lang
et al., 2010).
Objects may exist as bona fide objects or as fiat objects (Smith
and Varzi, 2000), thus they exist without or with human appreciation
(Castilla and Hay, 2008). Intuitively we may think of bona fide
objects sensu geographical entities as the primary target objects of
an image analysis task, i.e. entities that can be clearly delineated
by human vision and assigned critical spectral or geometrical features
(cf. Figs. 4 and 6).
As a branch of philosophy, ontology studies the constituents of
reality. An ontology of a given domain describes the constituents of
reality within that domain in a systematic way, as well as the relations
between these constituents and the relations of these to constituents
of other domains. Terms such as ‘domain’, ‘constituent’,
‘reality’ and ‘relation’ are themselves ontological terms, as are ‘feature’,
‘object’, ‘entity’, ‘item’, as well as ‘being’ and ‘existence’ themselves
(ibid.). Information scientists may use the term ‘ontology’
differently to philosophers. Typically, they designate the regimentation
of such conceptualizations through the development of tools
designed to render them explicit, such as point, line, and polygon,
etc. Geographical Information Systems typically impose simple
semantic structure about the world a-priori – mainly a textual
metadata description – or leave the semantics to the user. One
example may be a forest map: categories could be ‘deciduous’ or
‘‘coniferous’ or ‘commercial forest’ which can have totally different
meaning in different countries.
Smith and Mark (2001) argue for a top-level geographic domain
within their proposed general theory, with a pertinent basic level
category being land cover entities such as mountain, hill, island,
lake. Within GEOBIA we can also cope with land-cover categories
which are perceivable, yet not easily extractable based solely on
internal heterogeneity (so-called modelled composite classes, e.g.
an orchard). This links GEOBIA to GIScience from which other concepts
of continuous fields, discrete objects, and field objects (Yuan,
Fig. 6. Conceptual illustration of a multi-scale representation of an imaged landscape according to hierarchy theory principles. Object generation on the scale level of concern
(‘focal level’) is embedded in higher level objects and lower level ones. Note that within GEOBIA, higher hierarchical levels usually correspond to increasing average object
size. Objects have both self-integrative (‘part-of . . .’) and self-assertive (‘aggregates of . . .’) tendencies, and thereby feature the basic characteristics of holons. From Lang et al.
(2004).
188 T. Blaschke et al. / ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 180–191
2001) need to be adopted. In particular, GIScience may serve as a
theoretical underpinning for object fields (Cova and Goodchild,
2002) and the association classes of the object-oriented approach
to data modelling. We claim that GEOBIA concepts support and
also require semantics, which we illustrate in Fig. 7.
Until recently, the majority of GEOBIA methods started with the
identification of objects such as buildings or trees. Only recently,
methods are being developed which start from a combined spatial
semantics and thematic semantics approach of feature types, particularly
when addressing complex geospatial features. Tiede et al.
(2012) establish the implementation of a GEOBIA geoprocessing
service. Yue et al. (2013) propose a workflow-based approach for
discovery of complex geospatial features that uses geospatial
semantics and services. Andres et al. (2012) demonstrate how expert
knowledge explanation via ontologies can improve automation
of satellite image exploitation by starting from an image
ontology for describing image segments based on spectral, pseudo-spectral
and textural features. Arvor et al. (2013) comprehensively
portray ontologies in GEOBIA, especially for data discovery,
automatic image interpretation, data interoperability, workflow
management and data publication. This seems to become a now
trend in remote sensing and GIScience – namely first conceptualizing
real world classes and the starting analysis procedures.
5. Conclusions
This article builds the rationale for considering Geographic Object-Based
Image Analysis (GEOBIA) as a new and evolving paradigm
in remote sensing and to some degree in GIScience. It does
so by defining many of the key concepts. GEOBIA is strongly associated
with the notion of image segmentation but this article reveals
that this is only one but very typical geo-object-based
delineation strategy. GIS-like functionality is used in classification
procedures. This makes GEOBIA context-aware but also multisource
capable. When the methods become contextual they allow
for the utilization of ‘surrounding’ information and attributes. This
increases the importance of ontologies – as compared to the perpixel
analysis. The workflows are usually highly customizable or
adaptive allowing for the inclusion of human semantics and hierarchical
networks.
Given the diversity of geospatial data beyond images and the
necessity for multidisciplinary research, achieving efficient and
accurate data integration is fundamental to the effectiveness of
GEOBIA and may become a unique feature of GEOBIA compared
to other geospatial approaches. Researchers from biology, geography,
geology, hydrology and other disciplines need to access common
data sets and combine them with their discipline-specific
data. They also need to be able to load and share their thematic layers
‘‘intelligently’’. Furthermore, GEOBIA has to provide types of
models and forms of spatial analysis that are increasingly needed
to solve time sensitive social and environmental problems. Thus,
an evolving GEOBIA needs to provide solutions to integrate data
of widely varied quality, and spatio-temporal scales and
resolutions.
We found an increasing number of GEOBIA peer-reviewed publications,
special issues, books, commercial and free and open
source software, and specific job openings for experienced practitioners
etc. and we concluded that GEOBIA is an evolving paradigm.
Like other juvenile approaches we may still witness
terminological ambiguities. But based on the discussion of underlying
principles and methods we are confident that GEOBIA is
not just a collection of segmentation, analysis and classification
methods. It is an evolving paradigm with specific tools, software,
methods, rules, and language, and it is increasingly being used
for studies which need to conceptualize and formalize knowledge
representing location based reality.
Future research needs to transform GEOBIA databases into
more comprehensive (web-enabled) geographic knowledge-bases
supporting knowledge discovery and analysis far beyond classic
mapping, similar to recent GIS where scientific knowledge is or
should increasingly be based on the formalization of geospatial
Fig. 7. Principle of the iterative workflow in GEOBIA: Initially generated image-objects are classified and enhanced iteratively step-by-step by incorporating procedural and
object-domain knowledge described in an ontology and expressed and applied in a rule set.
T. Blaschke et al. / ISPRS Journal of Photogrammetry and Remote Sensing 87 (2014) 180–191 189
semantics and support for shared knowledge and collective intelligence
(Harvey and Raskin, 2011). This will facilitate the exploitation
of the enormous amounts of information currently residing
in images and image archives, transforming them into web accessible
value-added knowledge products. To reach this potential,
GEOBIA needs to adopt an appropriate, flexible and robust geospatial
digital earth model that allows for the linking/querying of multiscale
object attributes, and location traceable neighbourhoods
through time and over different mapping projections.
Acknowledgement
This research was partly funded by the Austrian Science Fund
FWF through the Doctoral College GIScience (DK W 1237-N23) as
well as through the University of Salzburg.
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