Several problems exist in polarimetric synthetic aperture radar (SAR) image classification

such as feature selection subjectivity and low utilization efficiency of massive features. Deep belief network (DBN) has a significant advantage in feature learning

which can be used in learning and extracting effective features from massive original features. Based on this observation

a polarimetric SAR image classification method based on DBN is proposed. The proposed method is capable of level-by-level learning and abstracting for the mass original polarimetric features. First

the original polarimetric feature sets are extracted from polarimetric SAR images. Second

20000 samples are selected

and feature vectors are constructed. Each pixel contains 267 original polarimetric features and class labels. Thus

a pixel is a sample

namely

a feature vector. The feature vector is used as input in the DBN model. Then

the DBN model is built to extract abstract features

namely

effective features. These features are achieved through level-by-level learning. Finally

the logistic regression

a classifier at the top of the DBN model

is applied to classify the entire polarimetric SAR image. Considering AIRSAR data as an example

the overall classification accuracy can reach a high accuracy of 91.06%. The DBN method shows outstanding advantage in feature learning. Simulation experiments show that compared with the traditional Wishart supervised classification algorithm

the DBN algorithm performs much better in classification. Simultaneously

the necessity of the DBN model has been proven by comparing with the logistic regression classification. The logistic regression classification classifies the polarimetric image using the original polarimetric features without any deep learning and extraction. In brief

the effectiveness of the DBN model has been validated through analysis and comparison. In this study

a novel polarimetric SAR image classification method is proposed. Mass polarimetric features of the polarimetric SAR image are utilized for the first time through the DBN. The advantages and applicability of the proposed method are analyzed. Overall

a novel method is proposed for polarimetric SAR image classification

which paves the way for further research and offer beneficial attempts for the utilization of DBN in polarimetric SAR image processing.