Deep Spatiotemporal Subpixel Mapping Network by Integrating a Prior Fine Land Cover Map With Change Detection
文献类型：期刊论文
| 作者 | Chen, Yuehong2; Huang, Jiamei2; Wang, Jiawei2; Zhou, Ya'nan2; Ge, Yong1 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 63页码:4412214 |
| 关键词 | Land surface Remote sensing Superresolution Semantic segmentation Spatial resolution Generators Spatiotemporal phenomena Radiometry Generative adversarial networks Deep learning Change detection generative adversarial networks (GANs) land cover prior fine land cover map semantic segmentation subpixel mapping (SPM) |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2025.3578351 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Subpixel mapping (SPM) is a prevailing technique for addressing the mixed pixel issue by estimating the subpixel-scale distribution of land cover types within mixed pixels. Recently, spatiotemporal SPM (STSPM) has advanced traditional SPM by integrating prior fine spatial resolution (FR) images to alleviate the uncertainty in SPM results. However, most existing STSPM methods are typically model-driven or meticulously handcrafted, and they usually struggle to flexibly characterize the diverse and complex patterns of land cover patches. This article proposes a novel deep STSPM network (STSPMNet) that integrates a prior FR land cover map with bitemporal coarse spatial resolution (CR) remote sensing images using deep learning and change detection techniques. STSPMNet first combines super-resolution and semantic segmentation to achieve the end-to-end SPM from CR remote sensing images to FR land cover maps. A change-detection-based optimization approach is then developed to incorporate a prior FR land cover map with the estimated FR land cover probabilities to generate the final FR land cover map at the prediction date. Three experiments were conducted to evaluate the effectiveness of the proposed STSPMNet. Experimental results demonstrate that STSPMNet outperforms two SPM methods and two STSPM method by recovering more spatial details of land cover patches and generating higher accuracy metrics. Hence, STSPMNet offers a robust solution for producing FR land cover maps from CR remote sensing imagery. |
| URL标识 | 查看原文 |
| WOS关键词 | HOPFIELD NEURAL-NETWORK ; SPATIAL-RESOLUTION ; PIXEL ; SCALE |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001515571100017 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215424]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Chen, Yuehong; Ge, Yong |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Hohai Univ, Coll Geog & Remote Sensing, Jiangsu Key Lab Soil & Water Proc Watershed, Nanjing 211100, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Chen, Yuehong,Huang, Jiamei,Wang, Jiawei,et al. Deep Spatiotemporal Subpixel Mapping Network by Integrating a Prior Fine Land Cover Map With Change Detection[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:4412214. |
| APA | Chen, Yuehong,Huang, Jiamei,Wang, Jiawei,Zhou, Ya'nan,&Ge, Yong.(2025).Deep Spatiotemporal Subpixel Mapping Network by Integrating a Prior Fine Land Cover Map With Change Detection.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,4412214. |
| MLA | Chen, Yuehong,et al."Deep Spatiotemporal Subpixel Mapping Network by Integrating a Prior Fine Land Cover Map With Change Detection".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):4412214. |
入库方式： OAI收割
来源：地理科学与资源研究所
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