RSSFormer: Foreground Saliency Enhancement for Remote Sensing Land-Cover Segmentatio | |
Rongtao Xu2,3; Changwei Wang2,3; Jiguang Zhang2; Shibiao Xu1; Weiliang Meng2; Xiaopeng Zhang2 | |
刊名 | IEEE Transactions on Image Processing
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2023-01 | |
卷号 | 32期号:无页码:1052-1064 |
关键词 | 遥感图像语义分割 |
ISSN号 | 1941-0042 |
DOI | 10.1109/TIP.2023.3238648 |
文献子类 | 期刊论文 |
英文摘要 | High spatial resolution (HSR) remote sensing images contain complex foreground-background relationships, which makes the remote sensing land cover segmentation a special semantic segmentation task. The main challenges come from the large-scale variation, complex background samples and imbalanced foreground-background distribution. These issues make recent context modeling methods sub-optimal due to the lack of foreground saliency modeling. To handle these problems, we propose a Remote Sensing Segmentation framework (RSSFormer), including Adaptive TransFormer Fusion Module, Detail-aware Attention Layer and Foreground Saliency Guided Loss. Specifically, from the perspective of relation-based fore ground saliency modeling, our Adaptive Transformer Fusion Module can adaptively suppress background noise and enhance object saliency when fusing multi-scale features. Then our Detail-aware Attention Layer extracts the detail and foreground related information via the interplay of spatial attention and channel attention, which further enhances the foreground saliency. From the perspective of optimization-based foreground saliency modeling, our Foreground Saliency Guided Loss can guide the network to focus on hard samples with low foreground saliency responses to achieve balanced optimization. Experimental results on LoveDA datasets, Vaihingen datasets, Potsdam datasets and iSAID datasets validate that our method outperforms existing general semantic segmentation methods and remote sensing segmentation methods, and achieves a good compromise between computational overhead and accuracy. |
URL标识 | 查看原文 |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/56670] ![]() |
专题 | 模式识别国家重点实验室_三维可视计算 |
通讯作者 | Shibiao Xu; Weiliang Meng |
作者单位 | 1.School of Artificial Intelligence, Beijing University of Posts and Telecommunications, China 2.State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, China 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, China |
推荐引用方式 GB/T 7714 | Rongtao Xu,Changwei Wang,Jiguang Zhang,et al. RSSFormer: Foreground Saliency Enhancement for Remote Sensing Land-Cover Segmentatio[J]. IEEE Transactions on Image Processing,2023,32(无):1052-1064. |
APA | Rongtao Xu,Changwei Wang,Jiguang Zhang,Shibiao Xu,Weiliang Meng,&Xiaopeng Zhang.(2023).RSSFormer: Foreground Saliency Enhancement for Remote Sensing Land-Cover Segmentatio.IEEE Transactions on Image Processing,32(无),1052-1064. |
MLA | Rongtao Xu,et al."RSSFormer: Foreground Saliency Enhancement for Remote Sensing Land-Cover Segmentatio".IEEE Transactions on Image Processing 32.无(2023):1052-1064. |
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