Abstract
This paper deals with coherent (in the sense that both amplitudes and relative phases of the polarimetric returns are used to construct the decision statistic) multipolarization synthetic aperture radar (SAR) change detection assuming the availability of reference and test images collected from $N$ multiple polarimetric channels. At the design stage, the change detection problem is formulated as a binary hypothesis testing problem, and the principle of invariance is used to come up with decision rules sharing the constant false alarm rate property. The maximal invariant statistic and the maximal invariant in the parameter space are obtained. Hence, the optimum invariant test is devised proving that a uniformly most powerful invariant detector does not exist. Based on this, the class of suboptimum invariant receivers, which also includes the generalized likelihood ratio test, is considered. At the analysis stage, the performance of some tests, belonging to the aforementioned class, is assessed and compared with the optimum clairvoyant invariant detector. Finally, detection maps on real high-resolution SAR data are computed showing the effectiveness of the considered invariant decision structures.
Highlights
A TECHNICAL challenge in synthetic aperture radar (SAR) signal processing is change detection, namely, the capability to identify temporal changes within a given scene [1]–[3] starting from a pair of coregistered images representing the area of interest, which is usually referred to as the reference and test pair
This means that, for each detector, after computing the decision statistics, the threshold has been selected in order that extended ground truth area, performances are given in terms of the number of detections belonging to KG, i.e., considering the cardinality of the set
Multipolarization SAR change detection has been considered in this paper
Summary
A TECHNICAL challenge in synthetic aperture radar (SAR) signal processing is change detection, namely, the capability to identify temporal changes within a given scene [1]–[3] starting from a pair of coregistered images representing the area of interest, which is usually referred to as the reference and test pair. Starting from the multipolarization data model developed in [9] and [10], we propose a new and systematic framework for change detection based on the theory of invariance in hypothesis testing problems [14], [15]. This is a viable means to force some desired properties to a decision statistic at the design stage and has already been successfully applied in some different radar detection problems [16]–[18].
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