A U-Net Based Land Use/Cover Change Detection Method with High Resolution Image

doi:10.13961/j.cnki.stbctb.2021.04.019

Home > Archive>Volume 41, Issue 4, 2021 >133-138,144. DOI:10.13961/j.cnki.stbctb.2021.04.019

A U-Net Based Land Use/Cover Change Detection Method with High Resolution Image
DOI:
                        10.13961/j.cnki.stbctb.2021.04.019
                    
Author:
                        Li CongyiLi Congyi
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan 454150, China;Key Laboratory of Soil and Water Conservation on the Loess Plateau of Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Zhengzhou, Henan 450000, China
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Kong XiangbingKong Xiangbing
Key Laboratory of Soil and Water Conservation on the Loess Plateau of Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Zhengzhou, Henan 450000, China
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Yang NaYang Na
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan 454150, China
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Wang YinanWang Yinan
Key Laboratory of Soil and Water Conservation on the Loess Plateau of Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Zhengzhou, Henan 450000, China
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Yang GangfengYang Gangfeng
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan 454150, China;Key Laboratory of Soil and Water Conservation on the Loess Plateau of Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Zhengzhou, Henan 450000, China
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Clc Number:P237
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Abstract:

[Objective] The U-Net based land use/cover change detection method with high resolution image was introduced to provide theoretical support for the application of the model in remote sensing image change detection. [Methods] The U-type neural network was used to detect the change spots in Gaofen-1 image of Yuzhou City, He’nan Province and WHU building data, and compared with FCN and SegNet. [Results] The experimental results showed that the F₁ score of U-type neural network model were 0.699,0.66 and 0.673 respectively, which were better than the other two methods, and the missing rate was lower, which was closer to the change reference diagram. [Conclusion] It is feasible to use U-type neural network for change detection in high-resolution remote sensing images, and it could have high detection accuracy.

Key words:change detection;high resolution remote sensing;U-type neural network;deep learning

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李聪毅,孔祥兵,杨娜,王逸男,杨刚凤.一种基于U-Net的高分影像土地利用/覆盖变化检测方法[J].水土保持通报英文版,2021,41(4):133-138,144

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Received:January 29,2021
Revised:April 25,2021
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Online: August 31,2021
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