SAR Target Recognition via Incremental Nonnegative Matrix Factorization

Remote Sensing

Sihang Dang¹  Zongyong Cui¹  Zongjie Cao¹  Nengyuan Liu¹ 

[1] Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China;

关键词: SAR target recognition;    incremental learning;    incremental NMF;    Lp sparse constraint;   

DOI  :  10.3390/rs10030374

来源: DOAJ

【 摘 要 】

In synthetic aperture radar (SAR) target recognition, the amount of target data increases continuously, and thus SAR automatic target recognition (ATR) systems are required to provide updated feature models in real time. Most recent SAR feature extraction methods have to use both existing and new samples to retrain a new model every time new data is acquired. However, this repeated calculation of existing samples leads to an increased computing cost. In this paper, a dynamic feature learning method called incremental nonnegative matrix factorization with L p sparse constraints (L p-INMF) is proposed as a solution to that problem. In contrast to conventional nonnegative matrix factorization (NMF) whereby existing and new samples are computed to retrain a new model, incremental NMF (INMF) computes only the new samples to update the trained model incrementally, which can improve the computing efficiency. Considering the sparse characteristics of scattering centers in SAR images, we set the updating process under a generic sparse constraint (L p) for matrix decomposition of INMF. Thus, L p-INMF can extract sparse characteristics in SAR images. Experimental results using Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data illustrate that the proposed L p-INMF method can not only update models with new samples more efficiently than conventional NMF, but also has a higher recognition rate than NMF and INMF.

【 授权许可】

Unknown