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Mask2Former with Improved Query for Semantic Segmentation in Remote-Sensing Images

文献类型：期刊论文

作者Guo, Shichen1,2; Yang, Qi2,3; Xiang, Shiming2,3; Wang, Shuwen4; Wang, Xuezhi1
刊名MATHEMATICS
出版日期2024-03-01
卷号12期号:5页码:24
关键词semantic segmentation remote-sensing image transformer Mask2Former query
DOI10.3390/math12050765
通讯作者Wang, Xuezhi(wxz@cnic.cn)
英文摘要Semantic segmentation of remote sensing (RS) images is vital in various practical applications, including urban construction planning, natural disaster monitoring, and land resources investigation. However, RS images are captured by airplanes or satellites at high altitudes and long distances, resulting in ground objects of the same category being scattered in various corners of the image. Moreover, objects of different sizes appear simultaneously in RS images. For example, some objects occupy a large area in urban scenes, while others only have small regions. Technically, the above two universal situations pose significant challenges to the segmentation with a high quality for RS images. Based on these observations, this paper proposes a Mask2Former with an improved query (IQ2Former) for this task. The fundamental motivation behind the IQ2Former is to enhance the capability of the query of Mask2Former by exploiting the characteristics of RS images well. First, we propose the Query Scenario Module (QSM), which aims to learn and group the queries from feature maps, allowing the selection of distinct scenarios such as the urban and rural areas, building clusters, and parking lots. Second, we design the query position module (QPM), which is developed to assign the image position information to each query without increasing the number of parameters, thereby enhancing the model's sensitivity to small targets in complex scenarios. Finally, we propose the query attention module (QAM), which is constructed to leverage the characteristics of query attention to extract valuable features from the preceding queries. Being positioned between the duplicated transformer decoder layers, QAM ensures the comprehensive utilization of the supervisory information and the exploitation of those fine-grained details. Architecturally, the QSM, QPM, and QAM as well as an end-to-end model are assembled to achieve high-quality semantic segmentation. In comparison to the classical or state-of-the-art models (FCN, PSPNet, DeepLabV3+, OCRNet, UPerNet, MaskFormer, Mask2Former), IQ2Former has demonstrated exceptional performance across three publicly challenging remote-sensing image datasets, 83.59 mIoU on the Vaihingen dataset, 87.89 mIoU on Potsdam dataset, and 56.31 mIoU on LoveDA dataset. Additionally, overall accuracy, ablation experiment, and visualization segmentation results all indicate IQ2Former validity.
资助项目Key Research Program of Frontier Sciences
WOS研究方向Mathematics
语种英语
WOS记录号WOS:001180975800001
出版者MDPI
资助机构Key Research Program of Frontier Sciences
源URL[http://ir.ia.ac.cn/handle/173211/57955]  
专题自动化研究所_模式识别国家重点实验室_遥感图像处理团队
通讯作者Wang, Xuezhi
作者单位1.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100083, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
4.Portland State Univ, Dept Comp Sci, Portland, OR 97201 USA
推荐引用方式
GB/T 7714		 Guo, Shichen,Yang, Qi,Xiang, Shiming,et al. Mask2Former with Improved Query for Semantic Segmentation in Remote-Sensing Images[J]. MATHEMATICS,2024,12(5):24.
APA Guo, Shichen,Yang, Qi,Xiang, Shiming,Wang, Shuwen,&Wang, Xuezhi.(2024).Mask2Former with Improved Query for Semantic Segmentation in Remote-Sensing Images.MATHEMATICS,12(5),24.
MLA Guo, Shichen,et al."Mask2Former with Improved Query for Semantic Segmentation in Remote-Sensing Images".MATHEMATICS 12.5(2024):24.

入库方式： OAI收割

来源：自动化研究所

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