期刊
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
卷 177, 期 -, 页码 238-262出版社
ELSEVIER
DOI: 10.1016/j.isprsjprs.2021.05.004
关键词
Semantic segmentation; Deep learning; Very-high-resolution imagery; Attention-fused network; ISPRS; Convolutional neural network
类别
资金
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19080302]
This paper proposes a multipath attention-fused network structure to address feature fusion challenges in semantic segmentation of remote sensing images. By fusing high-level abstract features and low-level spatial features, the network achieves state-of-the-art performance on two 2D datasets.
Semantic segmentation is an essential part of deep learning. In recent years, with the development of remote sensing big data, semantic segmentation has been increasingly used in remote sensing. Deep convolutional neural networks (DCNNs) face the challenge of feature fusion: very-high-resolution remote sensing image multisource data fusion can increase the network's learnable information, which is conducive to correctly classifying target objects by DCNNs; simultaneously, the fusion of high-level abstract features and low-level spatial features can improve the classification accuracy at the border between target objects. In this paper, we propose a multipath encoder structure to extract features of multipath inputs, a multipath attention-fused block module to fuse multipath features, and a refinement attention-fused block module to fuse high-level abstract features and low-level spatial features. Furthermore, we propose a novel convolutional neural network architecture, named attention-fused network (AFNet). Based on our AFNet, we achieve state-of-the-art performance with an overall accuracy of 91.7% and a mean F1 score of 90.96% on the ISPRS Vaihingen 2D dataset and an overall accuracy of 92.1% and a mean F1 score of 93.44% on the ISPRS Potsdam 2D dataset.
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