Automatic monitoring of natural resource in Anqing City of Anhui Province based on statistical learning methods-a case study of mountains

NI Huan; NIU Xiaonan; LI Yunfeng; HAO Jiaojiao

2021 Vol. 40, No. 10

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NI Huan, NIU Xiaonan, LI Yunfeng, HAO Jiaojiao. Automatic monitoring of natural resource in Anqing City of Anhui Province based on statistical learning methods-a case study of mountains[J]. Geological Bulletin of China, 2021, 40(10): 1656-1663.

Citation:

NI Huan, NIU Xiaonan, LI Yunfeng, HAO Jiaojiao. Automatic monitoring of natural resource in Anqing City of Anhui Province based on statistical learning methods-a case study of mountains[J]. Geological Bulletin of China, 2021, 40(10): 1656-1663.

Automatic monitoring of natural resource in Anqing City of Anhui Province based on statistical learning methods-a case study of mountains

1.
School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China
2.
Nanjing Center, China Geological Survey, Nanjing 210016, Jiangsu, China

More Information

Corresponding author: NIU Xiaonan, niuxiaonan0222@163.com

Received Date: 28 July 2020

Revised Date: 07 June 2021

Publish Date: 15 October 2021

Abstract

Abstract

Remote sensing, a technology used for quickly and extensively acquisition of land cover information, provides a reliable data source for complex natural resource survey.Aiming at the problem of mountain boundary recognition, an unsupervised statistical learning method was proposed to extract mountain features using remote sensing satellite images for modeling of mountain features.Specifically, DBSCAN algorithm and edge detection ideas were used to identify the mountain area and extract the mountain boundary.This approach recognizes the mountain boundary automatically, which does not rely on marking the ground truth manually.In the experiment, the Landsat 8 remote sensing satellite image data of Anqing City were used to effectively identify the mountainous area and extract the boundaries of the mountains.Through qualitative and quantitative analysis, the reliability of the proposed method was verified.Moreover, it proved the application potential of remote sensing technology and statistical learning theory in the field of natural resource survey.
- natural resources /
- mountain /
- remote sensing /
- statistical learning /
- recognition /
- Anhui Province

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References

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