Paper
15 October 2021 A deep learning training method of water identification based on the Third National Land Survey data
Zhida Chen, Chuan Lin, ChangLei Cao, Guang Gao, Liangzhong Ying
Author Affiliations +
Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119330T (2021) https://doi.org/10.1117/12.2615106
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China
Abstract
One of the biggest challenges in High Resolution Remote-Sensing Interpretation based on deep learning technology is the access of huge number of sample annotations. Currently, the sample annotation is carried out manually, which is a boring, time-consuming and labor-consuming work. Remote-Sensing Imagery comes from a variety of sources, each with its own independent features, so that there is insufficient quantity of sample annotations to cover most of these features. The Third National Land Survey (TNLS) data is a national land survey with high-quality manual annotation. This paper discusses the use of TNLS data instead of manual sample labeling for model training, and three hybrid methods of sample cleansing, data augmentation and training in turn, to create a deep learning training method of water identification. The results shown that the TNLS data with three hybrid methods was able to identify high-quality waters, the Frequency Weighted Intersection over Union (FWIOU) was up to 91.8%, which was 5.9% higher than the conventional training methods. The deep learning model used in this research was proposed to obtain the high accuracy and saved a lot of human resources, which had a wide range of practicability.
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhida Chen, Chuan Lin, ChangLei Cao, Guang Gao, and Liangzhong Ying "A deep learning training method of water identification based on the Third National Land Survey data", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119330T (15 October 2021); https://doi.org/10.1117/12.2615106
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KEYWORDS
Remote sensing

Data modeling

Image segmentation

Performance modeling

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