A hierarchical progressive recognition network for building change detection in high‐resolution remote sensing images

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-08-26 DOI:10.1111/mice.13330

Zhihuan Liu, Zaichun Yang, Tingting Ren, Zhenzhen Wang, JinSheng Deng, Chenxi Deng, Hongmin Zhao, Guoxiong Zhou, Aibin Chen, Liujun Li

{"title":"A hierarchical progressive recognition network for building change detection in high‐resolution remote sensing images","authors":"Zhihuan Liu, Zaichun Yang, Tingting Ren, Zhenzhen Wang, JinSheng Deng, Chenxi Deng, Hongmin Zhao, Guoxiong Zhou, Aibin Chen, Liujun Li","doi":"10.1111/mice.13330","DOIUrl":null,"url":null,"abstract":"Building change detection (BCD) plays a crucial role in urban planning and development. However, several pressing issues remain unresolved in this field, including false detections of buildings in complex backgrounds, the occurrence of jagged edges in segmentation results, and detection blind spots in densely built‐up areas. To address these challenges, this study innovatively proposes a Hierarchical Adaptive Gradual Recognition Network (HAGR‐Net) to improve the accuracy and robustness of BCD. Additionally, this research is the first to employ the Reinforcement Learning Optimization Algorithm Based on Particle Swarm (ROPS) to optimize the training process of HAGR‐Net, thereby accelerating the training process and reducing memory overhead. Experimental results indicate that the optimized HAGR‐Net outperforms state‐of‐the‐art methods on the WHU_CD, Google_CD, and LEVIR_CD data sets, achieving F1 scores of 93.13%, 85.31%, and 91.72%, and mean intersection over union (mIoU) scores of 91.20%, 85.99%, and 90.01%, respectively.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"13 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer-Aided Civil and Infrastructure Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/mice.13330","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

Abstract

Building change detection (BCD) plays a crucial role in urban planning and development. However, several pressing issues remain unresolved in this field, including false detections of buildings in complex backgrounds, the occurrence of jagged edges in segmentation results, and detection blind spots in densely built‐up areas. To address these challenges, this study innovatively proposes a Hierarchical Adaptive Gradual Recognition Network (HAGR‐Net) to improve the accuracy and robustness of BCD. Additionally, this research is the first to employ the Reinforcement Learning Optimization Algorithm Based on Particle Swarm (ROPS) to optimize the training process of HAGR‐Net, thereby accelerating the training process and reducing memory overhead. Experimental results indicate that the optimized HAGR‐Net outperforms state‐of‐the‐art methods on the WHU_CD, Google_CD, and LEVIR_CD data sets, achieving F1 scores of 93.13%, 85.31%, and 91.72%, and mean intersection over union (mIoU) scores of 91.20%, 85.99%, and 90.01%, respectively.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

用于高分辨率遥感图像中建筑物变化检测的分层渐进式识别网络

建筑物变化检测（BCD）在城市规划和发展中发挥着至关重要的作用。然而，该领域仍有几个亟待解决的问题，包括复杂背景下的建筑物误检测、分割结果中出现锯齿状边缘以及建筑密集区的检测盲点。为应对这些挑战，本研究创新性地提出了分层自适应渐进识别网络（HAGR-Net），以提高 BCD 的准确性和鲁棒性。此外，本研究还首次采用了基于粒子群的强化学习优化算法（ROPS）来优化 HAGR-Net 的训练过程，从而加速训练过程并减少内存开销。实验结果表明，优化后的 HAGR-Net 在 WHU_CD、Google_CD 和 LEVIR_CD 数据集上的表现优于最先进的方法，F1 分数分别达到 93.13%、85.31% 和 91.72%，平均交集大于联合（mIoU）分数分别达到 91.20%、85.99% 和 90.01%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Computer-Aided Civil and Infrastructure Engineering 工程技术-工程：土木

CiteScore

17.60

自引率

19.80%

发文量

146

审稿时长

1 months

期刊介绍： Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms. Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.