The invention discloses a polarized SAR (
Synthetic Aperture Radar) image classifying method based on cooperative representation and
deep learning, and mainly solves the problems that an existing method is high in
computation complexity and low in classification precision. The method comprises the realizing steps: 1, inputting a polarized SAR image, and extracting the polarization characteristics of the image; 2, selecting a training sample set according to practical ground features, and selecting pixel points of the entire image as a
test sample set; 3, taking the characteristics of the training sample set as an initial dictionary, and learning the initial dictionary to obtain a learning dictionary by K-SVD (
Singular Value Decomposition); 4, synergically representing the training sample set and the testing sample set to obtain the representation coefficients of the training sample set and the testing sample set by the learning dictionary; 5, deeply learning the representation coefficients of the training sample set and the testing sample set so as to obtain more essential characteristic representing; and 6, carrying out the polarized SAR image classification on the representation coefficients by an libSVM (
Shared Virtual Memory) classifier after the
deep learning. The SAR image classifying method provided by the utility model is low in
computation complexity and high in classification accuracy, and is applicable to the polarized SAR image classification.