SAR Target Recognition via Incremental Nonnegative Matrix Factorization

Sihang Dang,Zongyong Cui,Zongjie Cao,Nengyuan Liu

doi:10.3390/rs10030374

Sihang Dang, Zongyong Cui + Show 2 more

Open Access

https://doi.org/10.3390/rs10030374

Copy DOI

Abstract

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.

Highlights

Synthetic aperture radar (SAR) is useful for ground observations, as it can work in the all time and weather conditions
Since feature extraction is a key process of synthetic aperture radar (SAR) automatic target recognition (ATR) [17], we aimed to develop a kind of incremental feature extraction method for SAR target features
There are two improvements in the proposed method: (1) the existing training model can be updated directly, reducing computational cost and improving efficiency when the number of training samples increases; and (2) Lp sparse constraints are added to the decomposition matrix during the update process, leading to more accurate solutions compared to general incremental NMF (INMF)

Summary

Introduction

Synthetic aperture radar (SAR) is useful for ground observations, as it can work in the all time and weather conditions. The most recent works in SAR target recognition use images of training samples to obtain a more than 90% recognition rate without any pretreatment [1,2,3]. In SAR target recognition, new training samples are obtained continuously, and as such there is a need for the feature model to be updated in real time. As the sample number increases, they can train suitable models when the number of samples is 110, 120, 130, and so on later Using both existing and new samples to retrain a new model every time results in a linear increase in computation costs. This leads to reduced data processing efficiency. One approach to solving this problem is by developing an incremental learning method to replace the traditional methods

Objectives

Methods

Findings

Discussion

Conclusion