Feature extraction for SAR target recognition based on supervised manifold learning

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Du, C.^1 ; Zhou, S.^1 ; Sun, J.^1 ; Zhao, J.^1

College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, China^1

关键词: Class separability;    Discriminating power;    Feature extraction algorithms;    Feature extraction methods;    Local tangent space;    Nearest-neighbour classifier;    Supervised manifold learning;    Uncorrelated constraints;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012241/pdf
DOI  :  10.1088/1755-1315/17/1/012241

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

On the basis of manifold learning theory, a new feature extraction method for Synthetic aperture radar (SAR) target recognition is proposed. First, the proposed algorithm estimates the within-class and between-class local neighbourhood surrounding each SAR sample. After computing the local tangent space for each neighbourhood, the proposed algorithm seeks for the optimal projecting matrix by preserving the local within-class property and simultaneously maximizing the local between-class separability. The use of uncorrelated constraint can also enhance the discriminating power of the optimal projecting matrix. Finally, the nearest neighbour classifier is applied to recognize SAR targets in the projected feature subspace. Experimental results on MSTAR datasets demonstrate that the proposed method can provide a higher recognition rate than traditional feature extraction algorithms in SAR target recognition.