Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline
8. Conclusion
4.2. Object-based classification
The goal of OBIA is to partition RS images into meaningful image- objects as defined above. Since some degree of oversegmentation is desir- able (see below), most meaningful image-objects will consist of aggregates of segments, formed via object-based classification. The latter is the proc- ess of associating initial image-objects (segments) to geo-object classes, based on both the internal features of the objects and their mutual relation- ships. Ideally, there should be a one-to-one correspondence between im- age-segments and meaningful image-objects. However, this is hardly at- tainable, since a semantic model (i.e., a classification scheme) of the landscape cannot be projected with complete success onto regions (seg- ments) that have been derived from a data-driven process (segmentation)15. There are at least two reasons for this shortcoming.
The first reason is that the relationship between radiometric similarity and semantic similarity is not straightforward. For example, there can be conspicuous differences in the image, such as those created by shadows, which have no meaning or no importance within the classification scheme, or conversely, the legend may include classes whose instances can barely be differentiated from each other in the image. In other cases, the way in which humans perceptually compose objects in the image, especially when
15 This statement refers to conventional segmentation algorithms. There are of coarse some automated procedures, such as template matching or cellular automata,which use external information to partition the image (and therefore are not data-driven). Integrating such model-driven techniques into OBIA should constitute a priority line of research.
some Gestalt principles16 intervene, may be too complex to be captured by a conventional dissimilarity metric. Therefore the possibility exists that the boundaries of the classified (aggregates of) image-objects do not lead to an agreeable representation of geo-objects. This implies that there will be some classified image-objects that need to be split or reshaped in part of their perimeter. The less clear the relation between the two similarities, the more likely this possibility. A way to tackle this problem is to stop the segmentation at a stage where these errors are less frequent or easier to fix.
This is why some degree of oversegmentation is desirable. In any case, the process of endowing image-objects with meaning is a complex one, likely requiring several cycles of mutual interaction between segmentation and classification (Benz et al. 2004), regardless of what segmentation method is used.
The second reason, which has already been implicitly suggested, is that given a segmentation derived from an image of a certain spatial resolution, and even assuming that each segment corresponds to some recognizable entity, it is highly unlikely that all delineated objects belong to the same hierarchical level. For example, in a segmented 1 m resolution image, there can be segments that correspond to individual trees and cars, but also to whole meadows and lakes. While ‘meadow’ and ‘lake’ may have been included as separate classes in the legend, trees and cars are probably sub- objects of some class like ‘urban park’ and ‘parking lot’. Therefore, some aggregation of image-objects according to semantic rules will be required.
In addition, for reasons explained in section 3.2, it might be necessary to upscale the image and segment it at a coarser resolution, since some geo-objects (e.g., a city) have boundaries that are indistinguishable at fine scales. The latter operation involves a further complication: the linkage of image-objects derived at different resolutions, since in general they will not nest seamlessly. At the moment there is no consensus on how to make this linkage. However, we note that a related problem has been addressed in Computer Vision regarding the tracking of moving objects across video frames, referred to as the correspondence problem (Cox 1993). This is akin to matching ambiguities between image-objects in successive images of increasingly larger pixel size.
16 Basic principles describing the composing capability of our senses, particularly with respect to the visual recognition of objects and whole forms instead of just a collection of individual patterns (Wertheimer 1923).
5 Summary
OBIA is a relatively new approach to the analysis of RS images where the basic units, instead of being individual pixels, are image-objects. An im- age-object is a discrete region of a digital image that is internally coherent and different from their surroundings, and that potentially represents – alone or in assemblage with other neighbors- a geo-object. The latter is a bounded geographic region that can be identified for a period of time as the referent of a geographic term such as those used in map legends.
Within OBIA, image-objects are initially derived from a segmentation al- gorithm and then classified using both their internal features and their mu- tual relationships. Image-objects are devoid of meaning until they are for- mally recognized as representational instances of either constituents parts of geo-objects or of whole geo-objects. Recognition involves the projec- tion of a semantic model (taxonomy) onto a representation (the segmented image) of the landscape, where the result is a partonomy, i.e., a partition into meaningful image-objects (which mostly are aggregates of segments, i.e., the initial image-objects), each representing a single geo-object. The instantiation of image-objects as (representations of) geo-objects is a com- plex process for which we need to develop, and agree upon a comprehen- sive ontology. Here we have proposed definitions and relationships for two basic terms of it: ‘image-object’ and ‘geo-object’.
Acknowledgements
This research and Dr Castilla postdoctoral fellowship have been gener- ously supported in grants to Dr Hay from the University of Calgary, the Alberta Ingenuity Fund, and the Natural Sciences and Engineering Re- search Council (NSERC). The opinions expressed here are those of the Authors, and do not necessarily reflect the views of their funding agencies.
We also thank an anonymous reviewer for helpful comments.
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