SAR Image Recognition Combined Bidirectional 2DPCA with PCA

Abstract

The method of Principal Component Analysis (PCA) needs to convert image matrix to high-dimensional column vector used in feature extraction. The 2-dimensional PCA (2DPCA) offsets disadvantages of PCA. However, 2DPCA compresses image along the rows or columns only, the number of features is still large. In order to solve the above problems, bidirectional 2DPCA was used to compress image matrix along row and column meanwhile, then use PCA reduce the number of computations and feature dimensions. Three kinds of ground static military targets images acquired by SAR were used as the experimental data. The experimental result shows that, the method of SAR image recognition presented by this paper reduced the dimensions of feature matrix and raised the recognition rate.