Light & Engineering 31 (5)

Volume 31
Date of publication 10/10/2023
Pages 135–142

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In-Terrestrial Aquaculture Fields Mapping from High Resolution Remote Sensing Images L&E, Vol.31, No.5, 2023

Articles authors:

Sujin Chen, Dmitry S. Efremenko, Zhiyuan Zhang, Lingkui Meng

Sujin Chen graduated from the School of Remote Sensing and Information Engineering, Wuhan University in 2020 with bachelor’s degree. She continued her postgraduate study in Wuhan University (WHU) from 2020 and took part in the Earth Oriented Space Science and Technology (ESPACE) program in Technical University of Munich since 2021. At present, she is doing MSc in WHU and TUM. Her scientific interests include remote sensing, web GIS, and machine learning

Dmitry S. Efremenko, Ph. D. He graduated from the Moscow Power Engineering institute (MPEI) in 2009. He received his Ph. D. degree from the Moscow State University in 2011 and the habilitation degree from MPEI in 2017. Since 2011 he works as a Research Scientist at the German Aerospace Centre (DLR). He is a docent at the Technical University of Munich. He has over 70 peer-reviewed publications. His scientific interests include radiate transfer, remote sensing, and machine learning

Zhiyuan Zhang, Ph. D. Since 2019 he works as engineer at the Information Center (Hydrology Monitor and Forecast Centre), Ministry of Water Resources. His research interests include remote-sensing application in water conservancy and geographic information systems

Lingkui Meng, Prof., Dr. Received his Ph. D. degree From Huazhong University of Science and Technology, Wuhan, China in 1994. He is currently appointed as Professor at the School of Remote Sensing and Information Engineering, Wuhan University. His scientific interests include remote sensing applications, web GIS, and computer architecture

Abstract:

Convolution neural networks are widely used for image processing in remote sensing. Aquacultures have an important role in food security and hence should be monitored. In this paper, a novel lightweight neural network for in-terrestrial aquaculture field retrieval from high-resolution remote sensing images is proposed. The structure of this pond segmentation network is based on the UNet architecture, providing higher training speed. Experiments are performed on Gaofen satellite datasets in Shanghai, China. The proposed network detects the inland aquaculture ponds in a shorter time than stateof-the-art neural network-based models and reaches an overall accuracy of about 90 %.

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Keywords

DOI: https://doi.org/10.33383/2023-009

Article in RUS:
Svetotekhnika, 2023, # 4, pp. 76–81

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