Dynamic High-Resolution Network for Semantic Segmentation in Remote-Sensing Images
文献类型：期刊论文
| 作者 | Guo, Shichen2,3; Yang, Qi1,3; Xiang, Shiming1,3 ; Wang, Pengfei2; Wang, Xuezhi2
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| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-04-26 |
| 卷号 | 15期号:9页码:28 |
| 关键词 | semantic segmentation remote-sensing image neural architecture search sparse regularization HRNet |
| DOI | 10.3390/rs15092293 |
| 通讯作者 | Wang, Xuezhi(wxz@cnic.cn) |
| 英文摘要 | Semantic segmentation of remote-sensing (RS) images is one of the most fundamental tasks in the understanding of a remote-sensing scene. However, high-resolution RS images contain plentiful detailed information about ground objects, which scatter everywhere spatially and have variable sizes, styles, and visual appearances. Due to the high similarity between classes and diversity within classes, it is challenging to obtain satisfactory and accurate semantic segmentation results. This paper proposes a Dynamic High-Resolution Network (DyHRNet) to solve this problem. Our proposed network takes HRNet as a super-architecture, aiming to leverage the important connections and channels by further investigating the parallel streams at different resolution representations of the original HRNet. The learning task is conducted under the framework of a neural architecture search (NAS) and channel-wise attention module. Specifically, the Accelerated Proximal Gradient (APG) algorithm is introduced to iteratively solve the sparse regularization subproblem from the perspective of neural architecture search. In this way, valuable connections are selected for cross-resolution feature fusion. In addition, a channel-wise attention module is designed to weight the channel contributions for feature aggregation. Finally, DyHRNet fully realizes the dynamic advantages of data adaptability by combining the APG algorithm and channel-wise attention module simultaneously. Compared with nine classical or state-of-the-art models (FCN, UNet, PSPNet, DeepLabV3+, OCRNet, SETR, SegFormer, HRNet+FCN, and HRNet+OCR), DyHRNet has shown high performance on three public challenging RS image datasets (Vaihingen, Potsdam, and LoveDA). Furthermore, the visual segmentation results, the learned structures, the iteration process analysis, and the ablation study all demonstrate the effectiveness of our proposed model. |
| WOS关键词 | NEURAL ARCHITECTURE SEARCH ; FRAMEWORK |
| 资助项目 | Key Research Program of Frontier Sciences, CAS[ZDBS-LY-DQC016] ; National Key Research and Development Program of China[2022YFF1301803] ; National Natural Science Foundation of China (NSFC)[62076242] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000987311100001 |
| 出版者 | MDPI |
| 资助机构 | Key Research Program of Frontier Sciences, CAS ; National Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53269]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Wang, Xuezhi |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100083, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Shichen,Yang, Qi,Xiang, Shiming,et al. Dynamic High-Resolution Network for Semantic Segmentation in Remote-Sensing Images[J]. REMOTE SENSING,2023,15(9):28. |
| APA | Guo, Shichen,Yang, Qi,Xiang, Shiming,Wang, Pengfei,&Wang, Xuezhi.(2023).Dynamic High-Resolution Network for Semantic Segmentation in Remote-Sensing Images.REMOTE SENSING,15(9),28. |
| MLA | Guo, Shichen,et al."Dynamic High-Resolution Network for Semantic Segmentation in Remote-Sensing Images".REMOTE SENSING 15.9(2023):28. |
入库方式： OAI收割
来源：自动化研究所
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