Most existing methods for change analysis aim at providing measurements that can be used to locate changes of phenomena of interest. This paper goes one step further and investigates a method that can be used to characterize and understand the spatial behavior of change by decomposing the change intensity image into a tree of entities called echelons. Such a tree can be extremely helpful in discovering connections between changes. Also, features can be computed from this tree and used in successive manual or automatic analysis. Results on synthetic and real-world change interpretation problems illustrate the power of the discussed method.
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