Today's high resolution remotely sensed images (<1m) pose several challenges which require solutions that go
beyond the traditional spectral based methodologies. With the rapid increase in the level of detail present in
these images, there is also an increase in the complexity. To deal with this complexity a consistent framework
and image representation is needed. An object-based scale-space representation is proposed. Principles of objectbased
design are explained and the application of these principles to image regions is introduced. Given an input
image, the scale-tree is automatically constructed using low-level information, starting with single pixels (as
objects) and ending with the root node indicating the complete image. The scale-tree is a hierarchical structure
where each level in the hierarchy differs from the next in the size of the objects/regions present at that level.
Hence, the scale-tree reflects the scale-space breakdown of the image. From another point of view the scaletree
can also be seen as a collection of multiple segmentations with varying level of detail going from fine to
coarse. Synthetic and real high resolution satellite images were used to evaluate our image representation. The
goal of the proposed representation is to facilitate applications such as target/anomaly detection, image region
classification and change detection.
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