基于卷积神经网络及遥感反演的生产建设项目识别方法及应用

doi:10.16843/j.sswc.2022161

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Abstract [Background] Due to the phenomenon of spectral similarities of foreign objects and spectral differences of the same objects in remote sensing images, object-oriented multi-scale and multi-threshold segmentation and classification methods cannot be used without significant manual intervention. This manual intervention results in high costs and low efficiency in the interpretation process. The purpose of this study is to establish a practical and efficient model for automatic identification of disturbed areas in production and construction projects, aiming to improve the efficiency and accuracy of disturbance identification. As one of the first batch of remote sensing monitoring demonstration provinces designated by the Ministry of Water Resources, Henan province has accumulated a significant amount of spatial data on soil and water conservation for production and construction projects. Therefore, this study selects Henan province as the research area. [Methods] The authors proposed an automatic identification method for production and construction projects based on neural networks and remote sensing inversion models. This method utilized AlexNet, which consists of 5 convolutional layers and 3 pooling layers, for initial automatic interpretation of disturbed spots. Additionally, remote sensing inversion models such as normalized difference vegetation index (NDVI) and temperature-vegetation dryness index (TVDI) were employed to further determine whether the identified spots were production and construction projects. Based on GF-1 and GF-6 remote sensing images taken in 2020, covering the entire Henan province, the author used the external rectangle of suspected production and construction project spot to crop the aforementioned images and obtain training samples. The samples were labeled and then input into the AlexNet model for training. After completing the training, two sets of tests were conducted using a single neural network model and a neural network model combined with remote sensing inversion. [Results] 1) This method performs better than a simple convolutional neural network recognition model in reducing both false recognition rate and missed recognition rate, with an average reduction of 23.88% in false recognition rate. It is particularly effective in reducing false recognition rate. 2) This method does not show any significant difference in terms of runtime cost compared to a simple convolutional neural network recognition model. [Conclusions] This achievement can effectively reduce the misclassification rate in the automatic identification of production and construction projects, contributing to the further improvement of the automation level in remote sensing monitoring of soil and water conservation in production and construction projects. It has certain reference value in the application field of automatic identification of production and construction projects.

Key words： convolutional neural network quantitative remote sensing the remote sensing supervision of soil and water conservation automatic identification soil and water conservation

Received: 28 July 2022

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TP753

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	LU Jingde
	WU Rongrong
	WU Peng
	DING Li

Cite this article:

LU Jingde,WU Rongrong,WU Peng, et al. A methodology of identifying production and construction projects by integrating convolutional neural networks with remote sensing inversion model and its application[J]. SSWC, 2024, 22(5): 199-208.

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http://journal12.magtechjournal.com/Jweb_stbc/EN/10.16843/j.sswc.2022161 OR http://journal12.magtechjournal.com/Jweb_stbc/EN/Y2024/V22/I5/199

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