首页|基于差异图构造与融合的SAR图像变化检测方法

基于差异图构造与融合的SAR图像变化检测方法

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  • NETL
  • NSTL
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针对合成孔径雷达(SAR)图像固有的相干斑点噪声而影响变化检测精度和准确性等问题，提出了一种基于差异图构造与融合的SAR图像变化检测方法。该方法通过L-SRAD混合滤波对SAR图像进行预处理，使用基于边缘预检测的小波融合算法实现对数双曲余弦比值差异图DCLR和邻域比值差异图DNR的融合，结合FCM算法和CWNN卷积神经网络对所得融合差异图进行变化检测。其中FCM算法将融合差异图预分类为三个聚类，选择合适的预分类结果作为训练样本训练CWNN模型，最后使用CWNN模型对预分类结果进行二次分类，得到最终的变化检测图。在Bern数据集上进行了对比实验，实验结果证明该方法具有较强的变化检测能力，变化检测准确率达到99。67％。
SAR image change detection method based on difference image construction and fusion
In view of the inherent coherent speckle noise in Synthetic-aperture radar(SAR)images,which af-fects the accuracy and accuracy of change detection,this paper proposes a change detection method for SAR images based on difference map construction and fusion.This method preprocesses SAR images through L-SRAD hybrid filte-ring,uses wavelet fusion algorithm based on edge pre-detection to achieve the fusion of logarithmic hyperbolic cosine ratio difference map DCLR and neighborhood ratio difference map DNR,and combines FCM algorithm and CWNN Convo-lutional neural network to detect changes in the fusion difference map.The FCM algorithm pre-classifies the fused difference map into three clusters,selects appropriate pre-classification results as training samples to train the CWNN model,and finally uses the CWNN model to perform secondary classification on the pre-classification results to obtain the final change detection map.Comparative experiments were conducted on the Bern dataset,and the experimental results showed that this method has strong change detection ability,with a change detection accuracy of 99.67％.

SAR change detectionL-SRAD filterlogarithmic hyperbolic cosinefocus wavelet fusionconvolutional wavelet neural network

林娇、火久元

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兰州交通大学电子与信息工程学院,兰州 730070

国家冰川冻土沙漠科学数据中心,兰州 730000

兰州瑞智元信息技术有限责任公司,兰州 730070

SAR变化检测 L-SRAD滤波器 对数双曲余弦比 改进的小波融合 卷积小波神经网络

国家自然科学基金甘肃省科技计划中小企业创新基金国家冰川冻土沙漠科学数据中心数据专题项目兰州交通大学百名青年优秀人才培养计划

6226203821CX6JA150

2024

10.14016/j.cnki.jgzz.2024.04.128

激光杂志
重庆市光学机械研究所

激光杂志

CSTPCD北大核心
影响因子：0.74
ISSN：0253-2743
年,卷(期)：2024.45(4)
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