Polarized SAR (Specific Absorption Rate) image classifying method based on super-vector coding

The invention discloses a polarized SAR (Specific Absorption Rate) image classifying method based on super-vector coding. The method comprises the following realizing steps: (1) inputting images; (2) refining Lee filter; (3) extracting a scattering characteristic vector; (4) coding super-sparsely; (5) obtaining super-vector characteristics; (6) normalizing and expanding the sample characteristic set; (7) selecting a training sample and a test sample; (8) training the classifier to classify the images; (9) calculating the classifying precision; (10) outputting a result. According to the method provided by the invention, the image characteristics extracted are unlikely to be affected by noise points, and are small in redundancy and good in representability, thereby being applicable to effectively improving the classification precision in the course of classifying, and detecting and identifying an SAR image objective of a polarized and synthetic aperture radar.