基于分层模糊聚类和小波卷积神经网络的SAR图像变化检测算法

doi:10.7523/j.ucas.2022.013

摘要/Abstract

摘要： 传统的合成孔径雷达(SAR)图像变化检测方法存在受散斑噪声影响大、图像深层信息难以利用、检测精度低等问题。针对以上问题,提出一种基于卷积神经网络和模糊聚类的SAR幅度图像变化检测算法。首先通过基于Gabor纹理的FLICM算法对差异图进行预分类,基于预分类结果自动选取可靠的训练样本,无需人工进行样本标注;引入多尺度通道注意力机制,并采用MSCA_WCNN完成二次分类,最终得到变化检测结果。本文算法在提取SAR图像不同尺度特征的同时能够对无关特征通道进行抑制,从而有效利用图像特征;小波卷积神经网络在保留图像有用信息的同时实现了去噪功能,增强了算法的鲁棒性。采用真实的星载SAR图像数据进行对比实验,实验结果表明,本文算法具有较高的检测精度,验证了算法的有效性。

关键词: SAR图像变化检测, 模糊聚类, 卷积神经网络, 多尺度卷积, 通道注意力

Abstract: Traditional synthetic aperture radar (SAR) image change detection methods have some problems, such as big impact by speckle noise, difficult to use deep information of the image, and low detection accuracy. To solve above problems, this paper presents an SAR amplitude image change detection algorithm based on convolution neural network and fuzzy clustering. Firstly, a hierarchical FLICM algorithm based on Gabor texture is used to pre-classify the difference images, and reliable training samples are automatically selected based on the pre-classification results without manual labeling. Then, a multiscale channel attention mechanism is introduced, and a MSCA_WCNN is used to complete the second classification, and the result of change detection is obtained. This algorithm extracts the different scale features of SAR images while suppressing the irrelevant feature channels to effectively utilize the image features. The wavelet convolution neural network achieves the denoising function while preserving the useful information of the image and enhances the robustness of the algorithm. The comparison experiments using real spaceborne SAR image data show that the algorithm has high detection accuracy and the effectiveness of the algorithm is verified.

Key words: SAR image Change detection, fuzzy clustering, CNN, multi-scale convolution, channel attention

中图分类号:

TP753

张萌, 潘志刚. 基于分层模糊聚类和小波卷积神经网络的SAR图像变化检测算法[J]. 中国科学院大学学报, 2023, 40(5): 637-646.

ZHANG Meng, PAN Zhigang. SAR image change detection algorithm based on hierarchical fuzzy clustering and wavelet convolution neural network[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(5): 637-646.

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