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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (4) : 244-250     DOI: 10.6046/gtzyyg.2020.04.30
Method research of intelligentized extraction of natural resources information from Shihe District,Xinyang City,Henan Province
WANG Yuefeng1,2(), WU Huizhi1,2, HE Shujun1,2, HUANG Di2,3, BAI Chaojun1,2
1. Henan Institute of Geological Survey, Zhengzhou 450001, China
2. Geological Remote Sensing Centre of National Engineering Lab for Satellite Remote Sensing Applications, Zhengzhou 450001, China
3. Henan Institute of Geological Sciences, Zhengzhou 450001, China
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Based on “Easy Interpretation” image processing software and using BJ-2 satellite remote sensing images, the method of “object-oriented+deep learning” was introduced into the intelligent information extraction and automatic classification of the 200 km 2 test plot in Shihe District, Xinyang City, which included forestland, tea garden, paddy land, water area, construction land and some other land. By the method of ratio vegetation index (RVI) in combination with the real-time selection of the boundary index threshold and eigenvalues, the forestland, tea garden and paddy land information of the test plot was classified intelligently. The water area information was extracted intelligently by the green and near-infrared band normalized difference vegetation index (NDVI). The information of construction land was extracted by using standard deviation of band1 as the eigenvalues. Based on the above methods and field geological survey, the results show that the intelligent information extraction in the test plot has a high accuracy of over 90%. The efficiency is 19 times higher than the traditional method. The study shows that “Easy Interpretation” image processing software is effective and highly accurate and can do half the work with twice the results, which has good value for extension and application in the intelligentized interpretation of natural resources and environment.

Keywords Easy Interpretation      natural resources      intelligentized extraction      automatically classification     
:  TP79  
Issue Date: 23 December 2020
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Yuefeng WANG
Huizhi WU
Shujun HE
Chaojun BAI
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Yuefeng WANG,Huizhi WU,Shujun HE, et al. Method research of intelligentized extraction of natural resources information from Shihe District,Xinyang City,Henan Province[J]. Remote Sensing for Land & Resources, 2020, 32(4): 244-250.
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Fig.1  BJ-2 B3(R),B2(G),B1(B) image of Shihe District and the location of test area
Fig.2  True color images composed of BJ-2 B3(R),B2(G),B1(B) of different features in test plot
Fig.3  Technical procedure of features extraction
Fig.4  Segmentation image of vegetation extraction for BJ-2 B3(R),B2(G),B1(B) image
Fig.5  Extraction result of forest land
Fig.6  Extraction result of paddy land
Fig.7  Extraction result of tea garden
Fig.8  Extraction result of the Nanwan Reservoir
Fig.9  Extraction results of construction land
序号 类别 图斑数/个 数量占比/% 面积/km2 面积占比/%
1 林地 2 281 11.72 144.02 67.51
2 茶园 4 124 21.19 37.77 17.70
3 水稻 1 222 6.28 12.26 5.75
4 建设用地 10 414 53.51 14.85 6.96
5 水体 1 420 7.30 4.43 2.08
合计 19 461 100.00 213.33 100.00
Tab.1  Statistics of intelligentized information extraction
序号 地类 图斑数/个 面积/km2 错漏数/个 勾绘不准确数/个 错误占比/% 正确率%
1 茶园 212 2.07 12 5 8.02 91.98
2 林地 39 8.31 1 2 7.70 92.30
3 水体 23 0.02 2 1 13.04 86.96
4 建设用地 319 0.30 14 6 6.27 93.73
合计/平均值 593 10.70 29 14 7.25 92.75
Tab.2  Statistics of the information extraction results of the south test plot
序号 地类 图斑数/个 面积/km2 错漏数/个 勾绘不准确数/个 错误占比/% 正确率/%
1 水稻田 546 2.98 15 25 7.33 92.67
2 林地 117 6.04 3 4 5.98 94.02
3 水体 110 0.19 4 5 8.18 91.82
4 建设用地 681 1.44 5 10 2.20 97.80
合计/平均值 1 454 10.65 27 44 4.88 95.12
Tab.3  Statistics of the information extraction results of the north test plot
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