Semantic segmentation of remote sensing images based on deep learning methods

Cong Huang; Yao Yang; Huajun Wang; Yu Ma; Jinquan Zhao; Jun Wan

doi:10.1117/12.2615120

15 October 2021 Semantic segmentation of remote sensing images based on deep learning methods

Cong Huang, Yao Yang, Huajun Wang, Yu Ma, Jinquan Zhao, Jun Wan

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 1193314 (2021) https://doi.org/10.1117/12.2615120
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

Remote sensing image segmentation has always been an important research direction in the field of remote sensing image processing, and it is a key step in the further understanding and analysis of remote sensing images. Image semantic segmentation is the process of classifying each pixel to form several sub-regions with respective characteristics, and extracting the objects of interest among them. However, due to the complex boundary and scale difference of the remote sensing image, the traditional algorithm can not meet the actual needs well, resulting in low segmentation accuracy. In order to further improve the accuracy of remote sensing image segmentation, this paper combines deep convolutional neural network with remote sensing image, based on the U-Net, firstly compares the model's segmentation accuracy under different learning strategies, and introduces a new learning strategy to improve the learning effect of the model; secondly, in the loss function part of the model, a new compound loss function is proposed to speed up the convergence of the network and improve the segmentation accuracy. Based on full experimental research on the WHDLD remote sensing image dataset, the results show that the improved method has 1.5% accuracy improvement compare to the U-Net.

Citation Download Citation

Cong Huang, Yao Yang, Huajun Wang, Yu Ma, Jinquan Zhao, and Jun Wan "Semantic segmentation of remote sensing images based on deep learning methods", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 1193314 (15 October 2021); https://doi.org/10.1117/12.2615120

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KEYWORDS

Image segmentation

Remote sensing

Data modeling

Image processing algorithms and systems

Performance modeling

Composites

Convolutional neural networks

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