SAR image classification method based on SAR-SIFT and DBN

The invention discloses a SAR image classification method based on SAR-SIFT and DBN. The problem of synthetic aperture radar image classification is mainly solved. The classification process comprises the following steps: (1) inputting an integer type SAR image matrix; (2) transforming the SAR image matrix; (3) extracting SAR-SIFT features; (4) max pooling; (5) normalization; (6) training four restricted Boltzmann machines RBM; (7) training a softmax classifier; (8) building a deep belief network DBN; (9) classification; and (10) calculating the classification accuracy. According to the invention, the SAR-SIFT features of synthetic aperture radar images are extracted, the deep belief network DBN is adopted, the features are learned layer by layer, the information integrity of the radar images is retained, depth information is mined, and a good classification effect is achieved. The method can be used for SAR image classification.