LULC-SegNet: Enhancing Land Use and Land Cover Semantic Segmentation with Denoising Diffusion Feature Fusion
文献类型：期刊论文
| 作者 | Shi, Zongwen2,3; Fan, Junfu3,4; Du, Yujie3; Zhou, Yuke4; Zhang, Yi1,5 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-12-01 |
| 卷号 | 16期号:23页码:4573 |
| 关键词 | land use and land cover semantic segmentation remote sensing images denoising diffusion probabilistic model feature fusion K-means clustering algorithms |
| DOI | 10.3390/rs16234573 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Deep convolutional networks often encounter information bottlenecks when extracting land object features, resulting in critical geometric information loss, which impedes semantic segmentation capabilities in complex geospatial backgrounds. We developed LULC-SegNet, a semantic segmentation network for land use and land cover (LULC), which integrates features from the denoising diffusion probabilistic model (DDPM). This network enhances the clarity of the edge segmentation, detail resolution, and the visualization and accuracy of the contours by delving into the spatial details of the remote sensing images. The LULC-SegNet incorporates DDPM decoder features into the LULC segmentation task, utilizing machine learning clustering algorithms and spatial attention to extract continuous DDPM semantic features. The network addresses the potential loss of spatial details during feature extraction in convolutional neural network (CNN), and the integration of the DDPM features with the CNN feature extraction network improves the accuracy of the segmentation boundaries of the geographical features. Ablation and comparison experiments conducted on the Circum-Tarim Basin Region LULC Dataset demonstrate that the LULC-SegNet improved the LULC semantic segmentation. The LULC-SegNet excels in multiple key performance indicators compared to existing advanced semantic segmentation methods. Specifically, the network achieved remarkable scores of 80.25% in the mean intersection over union (MIOU) and 93.92% in the F1 score, surpassing current technologies. The LULC-SegNet demonstrated an IOU score of 73.67%, particularly in segmenting the small-sample river class. Our method adapts to the complex geophysical characteristics of remote sensing datasets, enhancing the performance of automatic semantic segmentation tasks for land use and land cover changes and making critical advancements. |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001377644600001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/210441]  |
| 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 | Fan, Junfu |
| 作者单位 | 1.Shandong Agr & Engn Univ, Coll Land Resources & Surveying Engn, Zibo 255300, Peoples R China 2.Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China 3.Shandong Univ Technol, Sch Civil Engn & Geomat, Zibo 255000, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi, Zongwen,Fan, Junfu,Du, Yujie,et al. LULC-SegNet: Enhancing Land Use and Land Cover Semantic Segmentation with Denoising Diffusion Feature Fusion[J]. REMOTE SENSING,2024,16(23):4573. |
| APA | Shi, Zongwen,Fan, Junfu,Du, Yujie,Zhou, Yuke,&Zhang, Yi.(2024).LULC-SegNet: Enhancing Land Use and Land Cover Semantic Segmentation with Denoising Diffusion Feature Fusion.REMOTE SENSING,16(23),4573. |
| MLA | Shi, Zongwen,et al."LULC-SegNet: Enhancing Land Use and Land Cover Semantic Segmentation with Denoising Diffusion Feature Fusion".REMOTE SENSING 16.23(2024):4573. |
入库方式： OAI收割
来源：地理科学与资源研究所
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