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遥感技术与应用  2023, Vol. 38 Issue (5): 1107-1117    DOI: 10.11873/j.issn.1004-0323.2023.5.1107
1.黄河实验室(郑州大学),河南 郑州 450001
2.河南省出山店水库建设管理局,河南 信阳 464000
3.河南省地图院,河南 郑州 450003
Change Detection Method for Surface Cover of Heterogeneous Remote Sensing Image based on Code-Aligned Generative Adversarial Network
Chengcai ZHANG1(),Wei LIU1,Feng YANG2,Kai PENG1(),Xueli ZHOU3
1.Yellow River Laboratory (Zhengzhou University),Zhengzhou 450001,China
2.Chushandian Reservoir Management Bureau,Henan Province,Xinyang 464000,China
3.Henan Map Institution,Henan Province,Zhengzhou 450003,China
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关键词: 异源遥感影像变化检测自适应跨域映射    

Compared to the change detection of homologous remote sensing images, heterogeneous images can integrate the advantages of different satellite sensor data features and temporal relevance, better satisfying application requirements. To address the issues of spectral differences and inconsistent feature spaces in change detection of heterogeneous remote sensing images, this study proposes an aligned generative adversarial network for high-precision change detection of heterogeneous images. Considering the differences in channels and data types between heterogeneous images, it is difficult to maintain the consistency of spatial structures before and after reconstruction. The study incorporates autoencoders and constructs alignment loss to constrain the spatial structure changes of encoder output features, ensuring consistency in spatial structures between the reconstructed images and reducing information loss effectively. In the cross-domain mapping process, to minimize the color differences between source and target domain images, a cycle-consistent adversarial generative network is used for color transfer in the absence of paired images, enabling mutual cross-domain mapping between two temporally distinct heterogeneous images, generating color-preserving reconstructed images that can be directly compared with the original images. By utilizing designed change probability weights, the network automatically selects samples during the training process, effectively extracting land cover change information. Experimental results demonstrate that compared to methods such as CGAN and SCCN, the proposed method can more fully extract image features and reduce the randomness of cross-domain mapping functions. The detection accuracies on four publicly available datasets reach 0.93, 0.96, 0.97, and 0.88, with the highest accuracy achieved. The consistency between the change detection results and the reference maps, as well as the quality of the difference maps, is optimal. This method enables high-precision change detection in heterogeneous remote sensing images.

Key words: Heterogeneous remote sensing images    Change detection    Adaptive    Cross-domain mapping
收稿日期: 2022-08-27 出版日期: 2023-11-07
ZTFLH:  P237  
基金资助: 河南省自然科学基金项目“联合主动微波遥感和光学遥感数据的大型灌区土壤水分反演方法研究”(222300420539);河南省水利科技攻关项目“河南省出山店水库流域覆被遥感监测方法”(GG201902);国家自然科学基金重点项目“基于大数据的城市洪涝灾害预报预警理论与方法研究”(51739009)
通讯作者: 彭凯     E-mail:;
作者简介: 张成才(1964-),男,河南郸城人,博士,教授,主要从事水利信息技术研究。E?mail:
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张成才,刘威,杨峰,彭凯,周雪丽. 基于编码对齐生成对抗网络的异源遥感影像地表覆被变化检测方法[J]. 遥感技术与应用, 2023, 38(5): 1107-1117.

Chengcai ZHANG,Wei LIU,Feng YANG,Kai PENG,Xueli ZHOU. Change Detection Method for Surface Cover of Heterogeneous Remote Sensing Image based on Code-Aligned Generative Adversarial Network. Remote Sensing Technology and Application, 2023, 38(5): 1107-1117.


图1  变化检测流程图
图2  CAGAN结构(a)网络结构 (b)影像跨域回构过程



表1  CAGAN 的参数设置
图3  数据集
图4  数据集变化结果混淆图
图5  数据集差异图
表2  数据集1变化检测精度指标
表3  数据集2变化检测精度指标
表4  数据集3变化检测精度指标
表5  数据集4变化检测精度指标
图6  高斯滤波和阈值分割法评价结果图
表6  高斯滤波和阈值分割精度指标 (impact evaluation accuracy)
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