Multi-path cyclic residual network for semantic segmentation in remote sensing images

Xiaosuo Wu; Shuang Yao; Guocun Ge; Chaoyang Wu; Haowen Yan

doi:10.1117/12.3020920

23 January 2024 Multi-path cyclic residual network for semantic segmentation in remote sensing images

Xiaosuo Wu, Shuang Yao, Guocun Ge, Chaoyang Wu, Haowen Yan

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 1297802 (2024) https://doi.org/10.1117/12.3020920
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

Semantic feature extraction has become one of the hot research directions in remote sensing images, and the semantic segmentation technology of remote sensing images based on deep learning has achieved many results. However, with deepening of the network, semantic segmentation models based on deep learning have problems such as loss of detailed information and excessively increase of parameters, resulting in inaccurate extraction. To solve this problem, this paper devices a new type of Multi-Path Cyclic Residual Network. A Multi-Path Residual GCN is designed to enhances detailed features and reduces using of parameters. In addition, a Cyclic Residual Cross module is referenced to make the surrounding pixels aware of each other in the form of crossing. The model has achieved significant results on the published Postdam and Jiage datasets, and has also achieved significant improvements compared to other advanced models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaosuo Wu, Shuang Yao, Guocun Ge, Chaoyang Wu, and Haowen Yan "Multi-path cyclic residual network for semantic segmentation in remote sensing images", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 1297802 (23 January 2024); https://doi.org/10.1117/12.3020920

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