联合UNet++和多级差分模块的多源光学遥感影像对象级变化检测

doi:10.11947/j.AGCS.2023.20220202

测绘学报 ›› 2023, Vol. 52 ›› Issue (2): 283-296.doi: 10.11947/j.AGCS.2023.20220202

联合UNet++和多级差分模块的多源光学遥感影像对象级变化检测

王超¹, 王帅¹, 陈晓^1,4, 李俊勇¹, 谢涛^2,3

1. 南京信息工程大学电子与信息工程学院, 南京 210044;
2. 南京信息工程大学遥感与测绘工程学院, 南京 210044;
3. 青岛海洋科学技术国家实验室区域海洋学与数值模拟实验室, 青岛 266237;
4. 南京信息工程大学江苏省大气环境与装备技术协同创新中心, 南京 210044

收稿日期:2022-03-18 修回日期:2022-07-15 发布日期:2023-03-07
通讯作者: 谢涛 E-mail:xietao@nuist.edu.cn
作者简介:王超(1984-),博士,副教授,硕士生导师,主要研究方向为高分辨率遥感影像处理。E-mail:chaowang@nuist.edu.cn
基金资助:
国家重点研发计划（2021YFC2803302）；国家自然科学基金（42176180）；江苏省应急管理科技项目（YJGL-YF-2020-16）；江苏省自然资源发展专项（JSZRHYKJ202114）；江苏省博士后基金（2021K013A）；江苏省研究生科研与实践创新计划（SJCX22_0335）；江苏省六大人才高峰工程（2019XYDXX135）

Object-level change detection of multi-sensor optical remote sensing images combined with UNet++ and multi-level difference module

WANG Chao¹, WANG Shuai¹, CHEN Xiao^1,4, LI Junyong¹, XIE Tao^2,3

1. School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2. School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;
3. Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China;
4. Jiangsu Provincial Collaborative Innovation Center of Atmosphere Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China

Received:2022-03-18 Revised:2022-07-15 Published:2023-03-07
Supported by:
The National Key Research and Development Program of China (No. 2021YFC2803302);The National Natural Science Foundation of China project (No. 42176180);The Natural Science Foundation of Jiangsu Province(No. YJGL-YF-2020-16);The Natural Science Foundation of Jiangsu Province(No. JSZRHYKJ202114);The Post-doctoral fund of Jiangsu Province(No. 2021K013A);Postgraduate Research & Practice Innovation Program of Jiangsu Province(No. SJCX22_0335);The Six Talent-peak Project in Jiangsu Province (No. 2019XYDXX135)

摘要/Abstract

摘要： 随着传感器技术的飞速发展，基于多源光学遥感影像的变化检测已成为遥感领域中的研究热点。由于传感器成像差异，同一景象在多源光学遥感影像中通常呈现出不同的表现形式，因此面临着更加突出的“伪变化”问题。为此，本文提出了一种联合UNet++和多级差分模块的多源光学遥感影像对象级变化检测方法。该方法首先提出了一种多尺度特征提取差分（multi-scale feature extraction difference，MFED）模块，以增强模型对“伪变化”的识别能力；在此基础上，利用UNet++网络输出的多尺度特征对变化区域进行多角度精细刻画，并提出了一种自适应证据置信度指标（adaptive evidence credibility indicators，AECI）；最后结合影像分割与Dempster-Shafer （DS）理论设计了加权DS证据融合策略（weighted dempster shafer evidence fusion，WDSEF），从而实现了深度网络像素级输出至对象级结果的映射。通过对不同地区的4组高分多源光学影像数据集进行试验，并与多种先进的深度学习方法进行对比分析，结果表明：在不同空间分辨率和时相差异条件下，本文方法的总体精度（overall accuracy，OA）和F₁ score分别可达91.92%和63.31%以上，在目视分析和定量评价均显著优于对比方法。

关键词: 多源光学遥感影像, 变化检测, UNet++, 多尺度特征提取差分, 自适应证据信度指标, 加权DS证据融合

Abstract: With the rapid development of sensor technology, change detection based on multi-sensor optical remote sensing images has become a research hotspot in the field of remote sensing. Due to the differences of sensor imaging, different patterns of manifestation are shown in multi-sensor optical remote sensing images for one scene, leading to a more obvious problem of "pseudo change". Therefore, an object-level change detection method for multi-sensor optical remote sensing images combining UNet++ and multi-stage difference module is proposed in this paper. Firstly, multi-scale feature extraction difference (MFED) module is proposed by this method to enhance the ability of the model to identify "pseudo change". On this basis, multi-scale feature outputs by UNet++ network are used for multi-angle meticulous depiction. Adaptive evidence credibility indicator (AECI) is proposed as well. At last, image segmentation and Dempster-Shafer (DS) theory are combined to design weighted Dempster-Shafer evidence fusion (WDSEF), so as to achieve mapping from pixel-level output of deep network to object-level results. Experiment was conducted to four sets of high-resolution multi-sensor optical image datasets from different regions, and contrastive analysis was conducted to multiple methods of advanced deep learning. The results revealed that, the overall accuracy (OA) and F₁ score of the proposed method reached more than 91.92% and 63.31%, respectively, under different conditions of spatial resolution and temporal phase difference, which were significantly better than the comparison methods in both visual analysis and quantitative evaluation.

Key words: multi-sensor optical remote sensing image, change detection, UNet++, multi-scale feature extraction difference, adaptive evidence reliability index, weighted DS evidence fusion

中图分类号:

P237

王超, 王帅, 陈晓, 李俊勇, 谢涛. 联合UNet++和多级差分模块的多源光学遥感影像对象级变化检测[J]. 测绘学报, 2023, 52(2): 283-296.

WANG Chao, WANG Shuai, CHEN Xiao, LI Junyong, XIE Tao. Object-level change detection of multi-sensor optical remote sensing images combined with UNet++ and multi-level difference module[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(2): 283-296.

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联合UNet++和多级差分模块的多源光学遥感影像对象级变化检测

Object-level change detection of multi-sensor optical remote sensing images combined with UNet++ and multi-level difference module

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