An Adaptive and Adjustable Maximum-Likelihood Estimator for SAR Change Detection

Abstract

Synthetic aperture radar (SAR) change detection can be broadly classified into two categories: noncoherent intensity change detection and coherent change detection (CCD). The former methods, belonging to the field of image processing, are theoretically suitable for all SAR images since only utilize the information of SAR magnitude, however, the detection precision cannot be guaranteed. The latter methods can show effective performances for most SAR image pairs using identical collection geometrics because of the basis of the probability and statistics theory. In this paper, we propose a novel change estimator to combine the advantages of the two kinds of algorithms in a theoretical derivation way. An intensity-based estimator, inspired by the derivation of the coherence estimator in CCD, is first proposed. It is a new maximum-likelihood (ML) change estimator maximizing the probability distribution function (pdf) of the ratio change statistic instead of SAR complex data. In addition, the simple linear iterative cluster (SLIC) algorithm is introduced to make the new estimator adjustable because changes at different degrees can be extracted with varying settings of the superpixels number, which is further demonstrated in real SAR images. Finally, experiments in SAR image pairs of different statistical characteristics show that the proposed estimator can yield higher-contrast SAR change detection images than the other five common change statistics and obtain better change detection maps than the other four classic thresholding methods.