Abstract：[Background] Rapid acquiring the disturbance range of production and construction projects is critical for improving the efficiency of soil and water conservation supervision and inspection, and for controlling man-made soil erosion. The plots for production and construction projects are numerous and scattered, but remote sensing technology can achieve timely and accurate acquisition of their spatial distribution. At present, the remote sensing interpretation of disturbed patches in production and construction projects is mainly based on human-computer interaction with less automatic extraction. The existing algorithms are highly specialized and low in regional applicability, which makes it difficult applying in engineering.[Methods] The central and eastern parts of Gansu province were used as the research area, and deep learning algorithms were applied to carry out research on automatic extraction of disturbance areas. Based on GF-1 and GF-6 satellite images, the disturbance characteristics of production and construction projects were summarized, and bare land and newly added construction land were determined as the characteristic features detecting in production and construction projects. Selected 1 156 disturbances totally for the patch samples, then the enhanced DeepLabV3+ model and the U-Net model were utilized to construct an automatic extraction model of disturbance patches. Based on automatic extraction, the minimum recognition area was set to reduce the interference of mini patches. The extraction effects of different models and different minimum recognition areas were quantitatively evaluated by indicators such as quantity recall rate, quantity accuracy rate, area recall rate, and area accuracy rate.[Results] 1) The DeepLabV3+ model had fewer erroneous image spots. The quantity accuracy rate was more than 15% than that by the U-Net model, and the area accuracy rate was slightly higher than the U-Net model. 2) In Chengguan district and Zhengning county, the DeepLabV3+ model had fewer missing image spots, and the quantity recall rate was more than 5% higher than that of the U-Net model. The quantity recall rate of the DeepLabV3+ model in Baiyin district was slightly lower than that of the U-Net model. 3) Large patches extraction of these two models was better. The area accuracy rate was higher than the quantity accuracy rate, and the area recall rate was higher than the quantity recall rate. 4) As the minimum recognition area increased, the area accuracy of the model increased and the area recall rate decreased. 5) When the minimum recognition area was 1 hm2, utilizing the DeepLabV3+ model generated the best extraction effect. The average quantity accuracy rate was 78.31%, the quantity recall rate was 61.69%, the area accuracy rate was 88.82%, and the area recall rate was 80.60%.[Conclusions] In summary, DeepLabV3+ model has better overall classification effect. After setting an appropriate minimum recognition area, it can balance the accuracy and recall of the automatic extraction results. At the same time, the model has good extraction results in different districts and counties, which indicates that the deep learning algorithm has strong regional applicability and can be applied to a wide range of remote sensing supervision work.
康芮, 邱新玲, 马鸿财, 宋鹏宇. 基于深度学习的生产建设项目扰动图斑自动提取[J]. 中国水土保持科学, 2023, 21(1): 128-138.
KANG Rui, QIU Xinling, MA Hongcai, SONG Pengyu. Automatic extraction of disturbed patches in production and construction projects based on deep learning. SSWC, 2023, 21(1): 128-138.
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2023, Vol. 21 
