250328 Enhance publishing丨Enhanced Super-Resolution-based Dual-Path Short-Term Dense Concatenate Metric Change Detection Network for Heterogeneous Remote Sensing Images

Serious declaration: If this open source content is used in papers, books, academic reports, and other works, please cite the following references: LI Xi, ZENG Huaien, WEI Pengcheng. Enhanced Super-Resolution-based Dual-Path Short-Term Dense Concatenate Metric Change Detection Network for Heterogeneous Remote Sensing Images [J]. Journal of Electronics & Information Technology, in press. doi: 10.11999/JEIT250328Author: Li Xi, Zeng Huaien, Wei Pengcheng Unit: ① Hubei Three Gorges Landslide National Field Scientific Observation and Research Station;② School of Civil Engineering and Architecture, Three Gorges University; ③ Hubei Province Key Laboratory of Hydroelectric Engineering Construction and Management (Three Gorges University)DOI：10.11999/JEIT250328OL: https://jeit.ac.cn/cn/article/doi/10.11999/JEIT250328Corresponding author: Zeng Huaien, [e-mail] Open source date: December 31, 2025 Fund projects: National Natural Science Foundation of China (42074005), Open Fund Project of Hubei Provincial Key Laboratory of Hydropower Engineering Construction and Management (Three Gorges University) (2023KSD11), General Youth Fund Project of Hubei Provincial Natural Science Foundation (2025ABF104) Open source content 1. Change detection and reproduction code for dual path short-term dense connection measurement of heterogeneous remote sensing images based on enhanced super-resolution Abstract: There are problems with spatial resolution differences, spectral differences, and complex and diverse types of changes in optical heterogeneous high-resolution remote sensing images, making it more difficult to accurately and efficiently detect changes in heterogeneous high-resolution remote sensing images. A dual path short-term dense connection metric change detection network based on enhanced super-resolution for heterogeneous remote sensing images (ESR-DMSNet) is proposed to address the above issues, exploring a new path for high-precision and high-efficiency change detection of optical heterogeneous high-resolution remote sensing images. A heterogeneous remote sensing image quality optimization network based on enhanced super-resolution (ESRNet) is proposed, which enhances edge and detail information while addressing spatial resolution differences in heterogeneous remote sensing images at the image level; A dual path short-term dense connection metric change detection network (DSMNet) was proposed to address spectral differences in heterogeneous remote sensing images at the feature level and achieve high-precision and high-efficiency change detection; Comparative analysis of four sets of homologous and heterologous remote sensing image datasets shows that the proposed method outperforms the other 12 mainstream change detection methods, with F1 scores of 79.69%, 71.01%, 95.87%, and 90.55%, respectively. The proposed method has higher accuracy and efficiency, and the best generalization performance. When detecting large and small land features, the detection results are more consistent internally and have finer edges. The attachment is the open source code of the author's research findings.

郑重声明：若将本开源内容应用于论文、专著、学术报告等学术成果中，请引用如下文献：
李曦, 曾怀恩, 魏鹏程. 面向异质遥感影像的基于增强超分辨率的双路径短时密集连接度量变化检测网络[J]. 电子与信息学报, 已录用. DOI: 10.11999/JEIT250328
作者：李曦，曾怀恩，魏鹏程
单位：① 湖北三峡滑坡国家野外科学观测研究站；② 三峡大学土木与建筑学院；③ 水电工程施工与管理湖北省重点实验室（三峡大学）
DOI：10.11999/JEIT250328
在线获取地址：https://jeit.ac.cn/cn/article/doi/10.11999/JEIT250328
通讯作者：曾怀恩，邮箱：[e-mail]
开源日期：2025年12月31日
基金项目：国家自然科学基金（42074005）、水电工程施工与管理湖北省重点实验室（三峡大学）开放基金（2023KSD11）、湖北省自然科学基金一般青年项目（2025ABF104）
开源内容
1. 基于增强超分辨率的异质遥感影像双路径短时密集连接度量变化检测与复现代码
摘要：光学异质高分辨率遥感影像存在空间分辨率差异、光谱差异以及变化类型复杂多样等问题，导致异质高分辨率遥感影像的精准高效变化检测难度大幅提升。针对上述问题，本文提出面向异质遥感影像的基于增强超分辨率的双路径短时密集连接度量变化检测网络（ESR-DMSNet），为光学异质高分辨率遥感影像的高精度、高效率变化检测探索了新路径。提出基于增强超分辨率的异质遥感影像质量优化网络（ESRNet），在图像层面解决异质遥感影像空间分辨率差异问题的同时，增强影像的边缘与细节信息；提出双路径短时密集连接度量变化检测网络（DSMNet），在特征层面解决异质遥感影像的光谱差异问题，实现高精度、高效率的变化检测。通过四组同源与异源遥感影像数据集的对比分析，本文所提方法优于其余12种主流变化检测方法，F1值分别为79.69%、71.01%、95.87%和90.55%。所提方法具备更高的精度与效率，且泛化性能最优；在检测大小型地物时，检测结果内部一致性更强，边缘细节更为精细。附件为本研究团队研究成果的开源代码。