SAR remote sensing image segmentation algorithm based on improved DeepLabV3+

Zhu Sha; Ying Han; Shaofeng Ni; Jing Li; Xiaohan Li; Wenshuo Li; Yulin Wang

doi:10.1117/12.3048847

27 November 2024 SAR remote sensing image segmentation algorithm based on improved DeepLabV3+

Zhu Sha, Ying Han, Shaofeng Ni, Jing Li, Xiaohan Li, Wenshuo Li, Yulin Wang

Proceedings Volume 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024); 134020Q (2024) https://doi.org/10.1117/12.3048847
Event: International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 2024, Zhengzhou, China

Abstract

Due to the shape, size and diversity of water bodies in SAR remote sensing images as well as the complexity of the scene, the problem of loss of fine water bodies often occurs in the water body segmentation task, which leads to the low accuracy of water body segmentation. Therefore, in this paper, we improve on the DeeplabV3+ network. The network first uses the lightweight MobileNetV2 backbone network to reduce the number of parameters and computation of the net-work and to increase the model training speed. Secondly, in the encoder phase, the dense empty space pyramid pooling (DenseASPP) module is used to obtain denser feature pyramids and larger sensory fields, and bar pooling branches are added to enable the network to capture long-distance dependencies more efficiently and to improve the segmentation accuracy of the network for water bodies. Finally, in the decoder stage, the Attention Gate module is introduced before the fusion of low-level features and high-level features to suppress irrelevant information in the image and highlight im-portant features of the target. The improved network is named DSA-DeepLabV3+ network. The results of experiments performed on the home-made dataset show that the segmentation accuracy of DSA-DeepLabV3+ is better than DeepLabV3+ and its comparison methods.

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

Citation Download Citation

Zhu Sha, Ying Han, Shaofeng Ni, Jing Li, Xiaohan Li, Wenshuo Li, and Yulin Wang "SAR remote sensing image segmentation algorithm based on improved DeepLabV3+", Proc. SPIE 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 134020Q (27 November 2024); https://doi.org/10.1117/12.3048847

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KEYWORDS

Image segmentation

Convolution

Feature extraction

Remote sensing

Synthetic aperture radar

Education and training

Image processing algorithms and systems

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