SAR automatic target recognition using a hierarchical multi-feature fusion strategy

Abstract

A hierarchical feature fusion strategy based on Support Vector Machine (SVM) and Dempster-Shafer Evidence Theory is proposed for SAR image automatic target recognition in this paper. This strategy has three fusion hierarchies corresponding to three features. Principle Component Analysis (PCA), Local Discriminant Embedding (LDE) and Non-negative Matrix Factor (NMF) features are extracted from images without preprocessing, and are fed to SVM classifier. However, not all features are used in each fusion process. At each fusion process, an empirical threshold T is used to determine the used features and hierarchy depth. Experiments on MSTAR public data set demonstrate that the proposed strategy outperforms the system combining the outputs of three features directly.

SAR automatic target recognition based on a visual cortical system

“…[2] Use PCA to e xtract features and ART2 neural network to do the classification. [3] Use PCA, LDE, and NMF as mult i-features and use decision fusion strategies for SAR ATR. [4] Use scattering centers to do the feature matching.…”