Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification

Abstract

Classification of target microwave images is an important application in much areas such as security, surveillance, etc. With respect to the task of microwave image classification, a recognition algorithm based on aspect-aided dynamic non-negative least square (ADNNLS) sparse representation is proposed. Firstly, an aspect sector is determined, the center of which is the estimated aspect angle of the testing sample. The training samples in the aspect sector are divided into active atoms and inactive atoms by smooth self-representative learning. Secondly, for each testing sample, the corresponding active atoms are selected dynamically, thereby establishing dynamic dictionary. Thirdly, the testing sample is represented with ℓ1-regularized non-negative sparse representation under the corresponding dynamic dictionary. Finally, the class label of the testing sample is identified by use of the minimum reconstruction error. Verification of the proposed algorithm was conducted using the Moving and Stationary Target Acquisition and Recognition (MSTAR) database which was acquired by synthetic aperture radar. Experiment results validated that the proposed approach was able to capture the local aspect characteristics of microwave images effectively, thereby improving the classification performance.

Smooth non-negative sparse representation for face and handwritten recognition

“…They evaluate the performance of this technique on digit and face databases. In microwave image classification, a method called aspect-aided dynamic non-negative sparse representation was proposed by Zhang et al [35]. The authors attempt to classify active and inactive atoms via establishing a dynamic dictionary.…”