Hidden Feature-Guided Semantic Segmentation Network for Remote Sensing Images

被引:20
作者
Wang, Zhen [1 ]
Zhang, Shanwen [1 ]
Zhang, Chuanlei [2 ]
Wang, Buhong [3 ]
机构
[1] Xijing Univ, Sch Elect Informat, Xian 710123, Peoples R China
[2] Tianjin Univ Sci & Technol, Sch Artificial Intelligence, Tianjin 300457, Peoples R China
[3] AF Engn Univ, Sch Informat & Nav, Xian 710082, 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 mechanism; convolution neural networks (CNNs); hidden feature extraction; remote sensing image; semantic segmentation; FUSION NETWORK; ALGORITHM;
D O I
10.1109/TGRS.2023.3244273
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
For semantic segmentation of remote sensing images, convolutional neural networks (CNNs) have proven to be powerful tools. However, the existing CNN-based methods have the problems of feature information loss, serious interference by clutter information, and ignoring the correlation between different scale features. To solve these problems, this article proposes a novel hidden feature-guided semantic segmentation network (HFGNet) for remote sensing images, which achieves accurate semantic segmentation by hierarchically extracting and fusing valuable feature information. Specifically, the hidden feature extraction module (HFE-M) is introduced to suppress the salient feature representation to mine more valuable hidden features. Meanwhile, the multifeature interactive fusion module (MIF-M) establishes the correlation between different features to achieve hierarchical feature fusion. The multiscale feature calibration module (MSFC) is constructed to enhance the diversity and refinement representation of hierarchical fusion features. Besides, the local-channel attention mechanism (LCA-M) is designed to improve the feature perception capability of the object region and suppress background information interference. We conducted extensive experiments on the widely used ISPRS 2-D Semantic Labeling dataset and the 15-Class Gaofen Image dataset. Experimental results demonstrate that the proposed HFGNet has advantages over several state-of-the-art methods. The source code and models are available at https://github.com/darkseid-arch/RS-HFGNet.
引用
收藏
页数:17
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