Target recognition in synthetic aperture radar images via joint multifeature decision fusion

Abstract

Multifeature decision fusion is an effective way to promote the performance of target recognition of synthetic aperture radar (SAR) images. This paper proposes a joint multifeature decision fusion strategy for target recognition in SAR images based on multitask compressive sensing (MtCS). The proposed method can exploit the intercorrelations among different features by enforcing the constraint on the sparsity pattern. Furthermore, the time consumption for MtCS is almost the same with that of single feature-based compressive classification, such as sparse representation-based classification. Experiments on the moving and stationary target acquisition and recognition dataset and comparison with several state-of-the-art methods demonstrate the validity of the proposed method.