Toward Label‐Efficient Deep Learning Change Detection for Remote Sensing Imagery: A Comprehensive Review
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| Format: | Artículo Open Access |
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Wiley
2025
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| author | Daifeng Peng Min Liu Yongjun Zhang Haiyan Guan |
| author_facet | Daifeng Peng Min Liu Yongjun Zhang Haiyan Guan Daifeng Peng Min Liu Yongjun Zhang Haiyan Guan |
| collection | Wiley Open Access |
| contents | Toward Label‐Efficient Deep Learning Change Detection for Remote Sensing Imagery: A Comprehensive Review Daifeng Peng Min Liu Yongjun Zhang Haiyan Guan The Photogrammetric Record ABSTRACTThe rapid development of deep learning techniques has revolutionized various remote sensing applications, which is especially true for change detection (CD) areas. Consequently, the past few years have seen a surge of deep learning change detection (DLCD) techniques with unparalleled improvements in precision, efficiency, and automation. Despite their huge success, these methods often follow a data‐driven routine, where massive labeled data is required to guarantee network parameter learning. However, it is costly and labor‐intensive to obtain sufficient labeled data for the CD task, especially pixel‐level annotations. In this context, label‐efficient DLCD (LE‐DLCD) techniques have garnered increasing attention, which are capable of training CD networks with incomplete labels, inexact labels, or even no explicit labels. In this review, we conducted a comprehensive survey of state‐of‐the‐art label‐efficient DLCD methods, which are categorized into six schemes of semi‐supervised CD, weakly supervised CD, self‐supervised CD, active learning CD, few‐shot CD, and unsupervised CD. Subsequently, each scheme is further categorized into finer subcategories for in‐depth summarization and analysis. Next, we make systematic quantitative comparisons of typical LE–DLCD methods to provide valuable guidance for real‐world scenarios. Finally, the challenges and future directions of LE‐DLCD are presented in detail, which aims to shed light and inspiration on this area for the CD community. To facilitate transparency, we have shared the selected LE‐DLCD methods via https://github.com/daifeng2016/Awesome‐Label‐efficient‐Deep‐Learning‐Change‐Detection‐Methods. 10.1111/phor.70021 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1111/phor.70021 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_phor_70021 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Toward Label‐Efficient Deep Learning Change Detection for Remote Sensing Imagery: A Comprehensive Review Daifeng Peng Min Liu Yongjun Zhang Haiyan Guan The Photogrammetric Record Toward Label‐Efficient Deep Learning Change Detection for Remote Sensing Imagery: A Comprehensive Review Daifeng Peng Min Liu Yongjun Zhang Haiyan Guan The Photogrammetric Record ABSTRACTThe rapid development of deep learning techniques has revolutionized various remote sensing applications, which is especially true for change detection (CD) areas. Consequently, the past few years have seen a surge of deep learning change detection (DLCD) techniques with unparalleled improvements in precision, efficiency, and automation. Despite their huge success, these methods often follow a data‐driven routine, where massive labeled data is required to guarantee network parameter learning. However, it is costly and labor‐intensive to obtain sufficient labeled data for the CD task, especially pixel‐level annotations. In this context, label‐efficient DLCD (LE‐DLCD) techniques have garnered increasing attention, which are capable of training CD networks with incomplete labels, inexact labels, or even no explicit labels. In this review, we conducted a comprehensive survey of state‐of‐the‐art label‐efficient DLCD methods, which are categorized into six schemes of semi‐supervised CD, weakly supervised CD, self‐supervised CD, active learning CD, few‐shot CD, and unsupervised CD. Subsequently, each scheme is further categorized into finer subcategories for in‐depth summarization and analysis. Next, we make systematic quantitative comparisons of typical LE–DLCD methods to provide valuable guidance for real‐world scenarios. Finally, the challenges and future directions of LE‐DLCD are presented in detail, which aims to shed light and inspiration on this area for the CD community. To facilitate transparency, we have shared the selected LE‐DLCD methods via https://github.com/daifeng2016/Awesome‐Label‐efficient‐Deep‐Learning‐Change‐Detection‐Methods. 10.1111/phor.70021 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Toward Label‐Efficient Deep Learning Change Detection for Remote Sensing Imagery: A Comprehensive Review |
| topic | The Photogrammetric Record |
| url | https://onlinelibrary.wiley.com/doi/10.1111/phor.70021 |