Local Feature Search Network for Building and Water Segmentation of Remote Sensing Image

被引:26
|
作者
Ma, Zhanming [1 ]
Xia, Min [1 ]
Weng, Liguo [1 ]
Lin, Haifeng [2 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Peoples R China
[2] Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Peoples R China
基金
中国国家自然科学基金;
关键词
semantic segmentation; building and water segmentation; local feature search; horizontal direction; high-resolution remote sensing image;
D O I
10.3390/su15043034
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Extracting buildings and water bodies from high-resolution remote sensing images is of great significance for urban development planning. However, when studying buildings and water bodies through high-resolution remote sensing images, water bodies are very easy to be confused with the spectra of dark objects such as building shadows, asphalt roads and dense vegetation. The existing semantic segmentation methods do not pay enough attention to the local feature information between horizontal direction and position, which leads to the problem of misjudgment of buildings and loss of local information of water area. In order to improve this problem, this paper proposes a local feature search network (DFSNet) application in remote sensing image building and water segmentation. By paying more attention to the local feature information between horizontal direction and position, we can reduce the problems of misjudgment of buildings and loss of local information of water bodies. The discarding attention module (DAM) introduced in this paper reads sensitive information through direction and location, and proposes the slice pooling module (SPM) to obtain a large receptive field in the pixel by pixel prediction task through parallel pooling operation, so as to reduce the misjudgment of large areas of buildings and the edge blurring in the process of water body segmentation. The fusion attention up sampling module (FAUM) guides the backbone network to obtain local information between horizontal directions and positions in spatial dimensions, provide better pixel level attention for high-level feature maps, and obtain more detailed segmentation output. The experimental results of our method on building and water data sets show that compared with the existing classical semantic segmentation model, the proposed method achieves 2.89% improvement on the indicator MIoU, and the final MIoU reaches 83.73%.
引用
收藏
页数:22
相关论文
共 50 条
  • [1] Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image
    Ding, Cheng
    Weng, Liguo
    Xia, Min
    Lin, Haifeng
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2021, 10 (04)
  • [2] Dense feature pyramid fusion deep network for building segmentation in remote sensing image
    Tian Qinglin
    Zhao Yingjun
    Qin Kai
    Li Yao
    Chen Xuejiao
    SEVENTH SYMPOSIUM ON NOVEL PHOTOELECTRONIC DETECTION TECHNOLOGY AND APPLICATIONS, 2021, 11763
  • [3] Multiscale Location Attention Network for Building and Water Segmentation of Remote Sensing Image
    Dai, Xin
    Xia, Min
    Weng, Liguo
    Hu, Kai
    Lin, Haifeng
    Qian, Ming
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 61
  • [4] Feature Guide Network With Context Aggregation Pyramid for Remote Sensing Image Segmentation
    Li, Jiaojiao
    Liu, Yuzhe
    Liu, Jiachao
    Song, Rui
    Liu, Wei
    Han, Kailiang
    Du, Qian
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2022, 15 : 9900 - 9912
  • [5] A Multi-Level Feature Fusion Network for Remote Sensing Image Segmentation
    Dong, Sijun
    Chen, Zhengchao
    SENSORS, 2021, 21 (04) : 1 - 18
  • [6] Remote Sensing Image Segmentation by Combining Feature Enhanced with Fully Convolutional Network
    Yu, Ruiguo
    Fu, Xuzhou
    Jiang, Han
    Wang, Chenhan
    Li, Xuewei
    Zhao, Mankun
    Ying, Xiang
    Shen, Hongqian
    NEURAL INFORMATION PROCESSING (ICONIP 2018), PT I, 2018, 11301 : 406 - 415
  • [7] SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation
    Hwang, Gyutae
    Jeong, Jiwoo
    Lee, Sang Jun
    REMOTE SENSING, 2024, 16 (17)
  • [8] Dual-Path Feature Aware Network for Remote Sensing Image Semantic Segmentation
    Geng, Jie
    Song, Shuai
    Jiang, Wen
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2024, 34 (05) : 3674 - 3686
  • [9] Multi-Resolution Transformer Network for Building and Road Segmentation of Remote Sensing Image
    Sun, Zhongyu
    Zhou, Wangping
    Ding, Chen
    Xia, Min
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2022, 11 (03)
  • [10] Local and global evaluation for remote sensing image segmentation
    Su, Tengfei
    Zhang, Shengwei
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2017, 130 : 256 - 276