SAR image segmentation method based on wavelet pooling convolutional neural networks

Abstract

The invention discloses an SAR image segmentation method based on wavelet pooling convolutional neural networks. The SAR image segmentation method comprises 1. constructing a wavelet pooling layer and forming wavelet pooling convolutional neural networks; 2. selecting image blocks and inputting the image blocks into the wavelet pooling convolutional neural networks, and training the image blocks; 3. inputting all the image blocks into the trained networks, and testing the image blocks to obtain a first class mark of an SAR image; 4. performing superpixel segmentation of the SAR image, and blending the superpixel segmentation result with the first class mark of the SAR image to obtain a second class mark of the SAR image; 5. obtaining a third class mark of the SAR image according to a Markov random field model, and blending the third class mark of the SAR image with the superpixel segmentation result to obtain a fourth class mark of the SAR image; and 6. blending the second class mark of the SAR image with the fourth class mark of the SAR image according to an SAR image gradient map to obtain the eventual segmentation result. The SAR image segmentation method based on wavelet pooling convolutional neural networks improves the segmentation effect of the SAR image and can be used for target detection and identification.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to a SAR image segmentation method, and can be used for target detection and recognition. Background technique [0002] With the rapid development of remote sensing technology, SAR image interpretation plays an increasingly important role in energy, environment, archeology and other aspects. SAR image segmentation is a basic and important part of SAR image interpretation, which can provide assistance for subsequent classification, detection, identification and tracking. The main goal of SAR image segmentation is to divide the SAR image into connected regions without intersection. To accomplish this goal, each pixel of the SAR image needs to be marked, so it is a very difficult task. [0003] The existing SAR image segmentation methods are mainly divided into feature-based methods and statistical model-based methods. The feature-based method is mainly to extract some features of...

AI Technical Summary

Problems solved by technology

However, the traditional pooling methods in convolutional neural networks, that is, maximum pooling and average pooling, simply take the maximum value or average value in the neighborhood window of the feature map, which will affect the structure of the learned features. Certain damage is not conducive to the segmentation of SAR images

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