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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (4) : 53-60     DOI: 10.6046/zrzyyg.2022280
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Extracting granite pegmatite information based on GF-2 images and the random forest algorithm
DU Xiaochuan1,2(), LOU Debo2(), XU Lingang1, FAN Yinglin1,2, ZHANG Lin1, LI Wanyue1,2
1. School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
2. MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
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Abstract  

Granite pegmatites serve as a significant carrier and prospecting marker of granite pegmatite-type lithium deposits. The southeastern Zhaka area in Tianjun County, Qinghai Province demonstrates considerable prospecting potential for lithium deposits. Nevertheless, its high altitudes and deep cross-cutting characteristics pose challenges in surface surveys. Hence, this study extracted the granite pegmatite information within the study area from remote sensing images using the random forest algorithm. With high-spatial-resolution GF-2 remote sensing images as the primary data source, it extracted the spectral, texture, exponential, topographic, and edge features from various ground objects within the study area. These features, together with the newly introduced contrast limited adaptive histogram equalization (CLAHE) features, constituted 25 feature variables, forming a feature subset. Then, feature variables in the subset were evaluated for their feature importance, and their importance scores were used for feature selection, determining the optimal feature combination for extracting granite pegmatite information. Ultimately, 16 feature variables were chosen for random forest classification, with the accuracy of the classification results assessed. The study indicates that: ①The CLAHE feature variables emphasize the tonal variations among ground objects, thereby enhancing the classification accuracy, with the overall accuracy increased by 2.7 percentage points and the Kappa coefficient increased by 0.035; ②The classification results for granite pegmatites based on GF-2 images and the random forest algorithm exhibited overall accuracy of 93.1%, with a Kappa coefficient of 0.902, user accuracy of 94.24%, and producer accuracy of 98.00%, confirming the effectiveness of the method used in this study. Moreover, this study provides reliable data for future research in the study area.
Keywords granite pegmatite      random forest algorithm      CLAHE      feature importance      Qinghai Province     
ZTFLH:  TP79  
Issue Date: 21 December 2023
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Xiaochuan DU
Debo LOU
Lingang XU
Yinglin FAN
Lin ZHANG
Wanyue LI
Cite this article:   
Xiaochuan DU,Debo LOU,Lingang XU, et al. Extracting granite pegmatite information based on GF-2 images and the random forest algorithm[J]. Remote Sensing for Natural Resources, 2023, 35(4): 53-60.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022280     OR     https://www.gtzyyg.com/EN/Y2023/V35/I4/53
Fig.1  Remote sensing image in the study area
波段 波段范围/μm 空间分辨率/m 幅宽/km 重访周期/d
Pan 0.45~0.90 1 45 5
Blue 0.45~0.52 4
Green 0.52~0.59
Red 0.63~0.69
NIR 0.77~0.89
Tab.1  Band parameters of GF-2 panchromatic multi-spectral camera
Fig.2  Image samples
参数名 取值范围 最优值
n_estimators 1,2,3,…,1 000 150
max_features Auto,Sqrt,Log2 Auto
max_depth 1,2,3,…,200 50
min_samples_split 2,3,…,100 4
min_samples_leaf 1,2,3,…,60 1
Tab.2  Optimal parameter combination
Fig.3  Feature importance ranking
Fig.4  Relationship between the number of features variables and test set accuracy
Fig.5  Curve of landmark spectrum
Fig.6  Results of landmark classification in the study area
地物类别 用户
精度/%		 生产者
精度/%		 错分
误差/%		 漏分
误差/%		 总体
精度/%		 Kappa
系数
花岗伟晶岩 94.24 98.00 5.76 2.00 93.10 0.902
道路 94.73 81.81 5.27 18.19
冰雪 98.04 98.04 1.96 1.96
阳坡裸地 94.44 95.13 5.56 4.87
干涸河流 81.61 73.19 18.39 26.81
阴坡裸地 93.23 94.98 6.77 5.02
Tab.3  Precision evaluation of the classification result
地物类别 用户
精度/%		 生产者
精度/%		 错分
误差/%		 漏分
误差/%		 总体
精度/%		 Kappa
系数
花岗伟晶岩 93.27 97.00 6.73 3.00 90.40 0.864
道路 94.74 85.71 5.26 14.29
冰雪 98.04 98.04 1.96 1.96
阳坡裸地 90.58 93.28 9.42 6.72
干涸河流 78.16 62.39 21.84 37.61
阴坡裸地 91.08 93.38 8.92 6.62
Tab.4  Precision evaluation of the 14 feature classification result
Fig.7  Image comparison of dry rivers
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