التفاصيل البيبلوغرافية
| العنوان: |
MCANet: A Multi-Branch Network for Cloud/Snow Segmentation in High-Resolution Remote Sensing Images. |
| المؤلفون: |
Hu, Kai1 (AUTHOR), Zhang, Enwei1 (AUTHOR), Xia, Min1 (AUTHOR) xiamin@nuist.edu.cn, Weng, Liguo1 (AUTHOR), Lin, Haifeng2 (AUTHOR) |
| المصدر: |
Remote Sensing. Feb2023, Vol. 15 Issue 4, p1055. 29p. |
| مصطلحات موضوعية: |
*HIGH resolution imaging, *FEATURE extraction, *REMOTE sensing, *IMAGE segmentation, *SNOW cover |
| مستخلص: |
Because clouds and snow block the underlying surface and interfere with the information extracted from an image, the accurate segmentation of cloud/snow regions is essential for imagery preprocessing for remote sensing. Nearly all remote sensing images have a high resolution and contain complex and diverse content, which makes the task of cloud/snow segmentation more difficult. A multi-branch convolutional attention network (MCANet) is suggested in this study. A double-branch structure is adopted, and the spatial information and semantic information in the image are extracted. In this way, the model's feature extraction ability is improved. Then, a fusion module is suggested to correctly fuse the feature information gathered from several branches. Finally, to address the issue of information loss in the upsampling process, a new decoder module is constructed by combining convolution with a transformer to enhance the recovery ability of image information; meanwhile, the segmentation boundary is repaired to refine the edge information. This paper conducts experiments on the high-resolution remote sensing image cloud/snow detection dataset (CSWV), and conducts generalization experiments on two publicly available datasets (HRC_WHU and L8 SPARCS), and the self-built cloud and cloud shadow dataset. The MIOU scores on the four datasets are 92.736%, 91.649%, 80.253%, and 94.894%, respectively. The experimental findings demonstrate that whether it is for cloud/snow detection or more complex multi-category detection tasks, the network proposed in this paper can completely restore the target details, and it provides a stronger degree of robustness and superior segmentation capabilities. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
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