Remote Sensing Semantic Segmentation via Boundary Supervision-Aided Multiscale Channelwise Cross Attention Network

被引:15
作者
Zheng, Jianwei [1 ]
Shao, Anhao [1 ]
Yan, Yidong [1 ]
Wu, Jie [1 ]
Zhang, Meiyu [1 ]
机构
[1] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Peoples R China
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2023年 / 61卷
基金
中国国家自然科学基金;
关键词
Attention module; boundary supervision (BS); convolutional neural network (CNN); remote sensing (RS); semantic segmentation;
D O I
10.1109/TGRS.2023.3292112
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
High spatial resolution (HSR) remote sensing (RS) images inevitably pose the challenge of multiscale transformation, as small objects, such as cars and helicopters (HCs), may occupy only a few pixel points. This incurs a significant hurdle for global context modeling, particularly in backbone networks with large downsampling coefficients. Simple summation or concatenation techniques, such as skip connections, fail to address semantic gaps and even impose negative impacts on multiscale feature fusion. Meanwhile, due to the complexity of foreground objects, the boundary details of HSR RS images are easy to lose in sampling operations. To overcome these challenges, we propose a multiscale channelwise cross attention network (MCCANet) assisted by boundary supervision (BS). Technically, MCCA captures the channel attention (CA) with various scales, which allows dynamic and adaptive feature fusion in a contextual scale-aware manner and focuses on both large and small objects distributed throughout the inputs. Besides, a channel and context strainer (CCS) module is proposed and embedded in MCCA, filtering channels and contexts for the mitigation of intraclass differences. In addition, we apply a BS module to recover boundary contour, avoiding the blurring effect during the construction of contextual information. The refined boundary allows for the effective recognition of surrounding pixels, ensuring a better segmentation performance. Extensive experiments on the instance segmentation in aerial images dataset (iSAID), International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam, and land-cover domain adaptive (LoveDA) datasets demonstrate that our proposed MCCANet achieves a good balance of high accuracy and efficiency. Code will be available at: https://github.com/ZhengJianwei2/MCCANet.
引用
收藏
页数:14
相关论文
共 60 条
  • [1] Chen LC, 2017, Arxiv, DOI [arXiv:1706.05587, DOI 10.48550/ARXIV.1706.05587]
  • [2] Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
    Chen, Liang-Chieh
    Zhu, Yukun
    Papandreou, George
    Schroff, Florian
    Adam, Hartwig
    [J]. COMPUTER VISION - ECCV 2018, PT VII, 2018, 11211 : 833 - 851
  • [3] DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
    Chen, Liang-Chieh
    Papandreou, George
    Kokkinos, Iasonas
    Murphy, Kevin
    Yuille, Alan L.
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (04) : 834 - 848
  • [4] FusionNet: Edge Aware Deep Convolutional Networks for Semantic Segmentation of Remote Sensing Harbor Images
    Cheng, Dongcai
    Meng, Gaofeng
    Xiang, Shiming
    Pan, Chunhong
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2017, 10 (12) : 5769 - 5783
  • [5] Chong Y., 2022, IEEE GEOSCI REMOTE S, V19, P1
  • [6] CCANet: Class-Constraint Coarse-to-Fine Attentional Deep Network for Subdecimeter Aerial Image Semantic Segmentation
    Deng, Guohui
    Wu, Zhaocong
    Wang, Chengjun
    Xu, Miaozhong
    Zhong, Yanfei
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [7] ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data
    Diakogiannis, Foivos, I
    Waldner, Francois
    Caccetta, Peter
    Wu, Chen
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2020, 162 (162) : 94 - 114
  • [8] LANet: Local Attention Embedding to Improve the Semantic Segmentation of Remote Sensing Images
    Ding, Lei
    Tang, Hao
    Bruzzone, Lorenzo
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 59 (01): : 426 - 435
  • [9] Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network
    Fu, Gang
    Liu, Changjun
    Zhou, Rong
    Sun, Tao
    Zhang, Qijian
    [J]. REMOTE SENSING, 2017, 9 (05)
  • [10] Dual Attention Network for Scene Segmentation
    Fu, Jun
    Liu, Jing
    Tian, Haijie
    Li, Yong
    Bao, Yongjun
    Fang, Zhiwei
    Lu, Hanqing
    [J]. 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, : 3141 - 3149