IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium | 2019
Bitemporal Fully Polarimetric Sar Images Change Detection Via Nearest Regularized Joint Sparse and Transfer Dictionary Learning
Abstract
Most current synthetic aperture radar (SAR) images change detection methods are developed based on a difference image. In this paper, we propose a novel bitemporal polarmetric SAR (PolSAR) images change detection framework based on their land-covers classifications. First, a nearest regularized joint sparse representation (NRJSR) model is developed to exploit the correlations among various polarimetric information and spatial context. Next, a transfer dictionary learning method is proposed for bitemporal PolSAR images classifications. Finally, the changed map can be obtained by comparing these two classification results. The comparison experiment results show that the proposed algorithm obtains better performance.