Classification of SAR Images Based on Deep Deconvolutional Network

Aim at the problem that the traditional feature extraction methods cannot get the high level structure features, this paper put forward a new soft probability pooling method, which is used in multilayer Deconvolutional Network, then high level structure features can be learned and be used for classification of SAR image. Firstly, the SAR image was divided into patches; then, the feature coding of each patch was obtained by means of multilayer Deconvolutional Networks, which can learn features suitable for image classification in different scale ; finally, the SAR image was classified through the features used in SVM classifier. Experimental results on the first batch domestic PolSAR images show that the classification accuracy rate of the proposed algorithm is superior.