Selection of Multi-Level Deep Features via Spearman Rank Correlation for Synthetic Aperture Radar Target Recognition Using Decision Fusion

Abstract

Convolutional neural networks (CNN) now become one of the most popular methods in synthetic aperture radar (SAR) target recognition. To fully exploit the deep features learned by CNN, this paper considers all the feature maps from different convolution layers. At each layer, the Spearman rank correlation is employed to evaluate the similarities between the feature maps and original SAR image. A certain proportion of feature maps with high similarities are selected and jointly represented based on the joint sparse representation (JSR) model. For the reconstruction error vectors from different layers, they are combined based on linear weighting using a random weight matrix. The fused reconstruction errors are analyzed to form a decision value for target recognition. The feature selection chooses the robust features and JSR considers the inner correlations between the feature maps from the same layer. In addition, the linear weighting using the random weight matrix could statistically reveal the correlations between the test sample and a certain training class. Therefore, the overall effectiveness and robustness of the proposed method can be enhanced. By performing experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset, the proposed method could achieve a very high average recognition rate of 99.32% for ten classes of ground targets under the standard operating condition (SOC). Furthermore, under the extended operating conditions (EOCs) like configuration differences, depression angle differences, noise corruption, and partial occlusion, the proposed could also achieve superior robustness over some state-of-the-art SAR target recognition methods.