Abstract: For the existence of speckle noise in Synthetic Aperture Radar (SAR) image,however,the traditional SAR image interpretation work is quite complicated.However,the image which is quality and visual effect obtained by the traditional SAR image retrieval method are not ideal of conception of it what is perfect most suitable.Therefore,the signal which is contained in the SAR image is not suitable.And the speckle distribution and texture-information are abundant in themselves.In order to improve the retrieval efficiency of SAR images,an image retrieval method is proposed that according to the visual features of the images,thereby improving the visual effect of the images and facilitating the artificial intuition to observe the images’ texture (cells) information; thus,using deep learning to take the advantages of fuzzy theory and neural network and to improve the performance of image processing.Firstly,according to the statistical characteristics of image pixel cells,according to the semantics of fuzzy neural network,an efficient image texture feature and Deep Learning semantic analysis method are proposed to classify and match the image texture style advantage.Se-condly,according to the semantic feature.The feature is shown that methods propose a retrieval of it.Firstly,the texture features of SAR images are extracted by Deep Learning Data demantic clustering,and then the SAR images are characterized according to the Synchronic Matrix method.Finally,the texture features of SAR images and the vector of filtered Gray-components are retrieved by Deep Learning method to perform Image Cells’classification.The experimental results show that the proposed method achieves preciser-results in SAR image retrieval,and the visual effects and analysis efficiency are better improved for analysis and application.Moreover,the method is effectivein suppressing speckle noise and visual effects on SAR image texture features.It’s an increasing strategy with the effects of SAR image analysis.

Key words: Data semantics, Deep learning neural network, Image segmentation, Synchronic matrix, Synthetic aperture radar, Texture feature