Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review
文献类型：期刊论文
| 作者 | Cheng, Guangliang1 ; Huang, Yunmeng2; Li, Xiangtai3; Lyu, Shuchang2; Xu, Zhaoyang4; Zhao, Hongbo2; Zhao, Qi2; Xiang, Shiming5
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| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-07-01 |
| 卷号 | 16期号:13页码:36 |
| 关键词 | change detection remote sensing algorithm granularity supervision modes comprehensive survey |
| DOI | 10.3390/rs16132355 |
| 通讯作者 | Cheng, Guangliang(guangliang.cheng@liverpool.ac.uk) |
| 英文摘要 | Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task. |
| WOS关键词 | IMAGE CHANGE DETECTION ; UNSUPERVISED CHANGE DETECTION ; AUTOMATIC CHANGE DETECTION ; SATELLITE IMAGES ; NETWORK ; EFFICIENT ; FUSION |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001269702200001 |
| 出版者 | MDPI |
| 源URL | [http://ir.ia.ac.cn/handle/173211/59269]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
| 通讯作者 | Cheng, Guangliang |
| 作者单位 | 1.Univ Liverpool, Dept Comp Sci, Liverpool L69 3BX, England 2.Beihang Univ, Dept Elect Informat Engn, Beijing 100191, Peoples R China 3.Peking Univ, Sch Intelligence Sci & Technol, Beijing 100871, Peoples R China 4.Univ Cambridge, Dept Paediat, Cambridge CB2 1TN, England 5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cheng, Guangliang,Huang, Yunmeng,Li, Xiangtai,et al. Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review[J]. REMOTE SENSING,2024,16(13):36. |
| APA | Cheng, Guangliang.,Huang, Yunmeng.,Li, Xiangtai.,Lyu, Shuchang.,Xu, Zhaoyang.,...&Xiang, Shiming.(2024).Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review.REMOTE SENSING,16(13),36. |
| MLA | Cheng, Guangliang,et al."Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review".REMOTE SENSING 16.13(2024):36. |
入库方式： OAI收割
来源：自动化研究所
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