Polarized SAR (Synthetic Aperture Radar) image classification method based on SLIC (Software Licensing Internal Code) and improved CNN (Convolutional Neural Network)

Abstract

The invention puts forward a polarized SAR (Synthetic Aperture Radar) image classification method based on an SLIC (Software Licensing Internal Code) and an improved CNN (Convolutional Neural Network), and is used for solving the technical problem of low classification speed and low classification accuracy in an existing supervised polarized SAR image classification method. The method comprises the following steps that: firstly, taking the Wishart distance and the polarization feature of a polarized SAR image as new data, carrying out Lee filtering on the new data, and inputting the new data into an improved CNN to be classified so as to obtain a preliminary classification result; then, carrying out SLIC superpixel segmentation on the pseudo-color image of the polarized SAR image to obtain a superpixel segmentation result; and finally, utilizing the superpixel segmentation result to carry out constraint post-processing on the preliminary classification result to obtain a final classification result. The method is high in classification speed and accuracy, and can be used for fields including polarized SAR terrain classification and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a polarimetric SAR image classification method, in particular to a polarimetric SAR image classification method fused with SLIC and an improved CNN, so that the convolutional neural network model can quickly perform polarimetric SAR data Accurate classification can be applied to ground object classification, target detection and target recognition of polarimetric SAR images. Background technique [0002] Image classification technology mainly uses the different features reflected in the image information to quantitatively analyze the image and attribute it to a certain category. The SAR image acquisition method is different from the natural image, which uses the relative motion between the imaged ground target and the radar to improve the azimuth resolution of the radar image. Each pixel of the SAR image contains not only the gray value reflecting the surface microwave re...

AI Technical Summary

Problems solved by technology

[0011] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art, propose a kind of polarimetric SAR image classification method based on improved CNN, combine SLIC and improved CNN to solve existing supervised polarimetric SAR image classification method The technical problems of slow classification speed and low classification accuracy

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