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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (2) : 37-46     DOI: 10.6046/zrzyyg.2021214
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Principal component selection method for hyperspectral remote sensing images based on spatial statistics
SUN Xiao1(), PENG Junhuan2(), ZHAO Feng3, WANG Xiaoyang1, LYU Jie2, ZHANG Dengfeng4
1. Langfang Natural Resources Comprehensive Survey Center, China Geological Survey, Langfang 065000, China
2. School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China
3. Urumqi Natural Resources Comprehensive Survey Center, China Geological Survey, Urumqi 830057, China
4. Xi’an Center of Mineral Resources Survey, China Geological Survey, Xi’an 710100, China
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Abstract  

The principal component analysis is a widely used method for dimensionality reduction of hyperspectral remote sensing images. In task-oriented work, the principal component selection method based on cumulative variance contribution rate is not ideal. To address the problem of principal component selection after principal component analysis transformation, a method of principal component selection based on spatial statistics is proposed. The selection of principal components is performed by calculating the values of the semi-variogram parameter range and partial sill/sill of each principal component. The magnitude of a range is used to judge the range of spatial correlation of each principal component, and the partial sill/sill is used to judge the strength of spatial correlation of each principal component. The simulation proves that the variable range and partial sill/sill can effectively express the range and strength of spatial correlation of hyperspectral remote sensing images. Based on the experiment of real hyperspectral remote sensing images, the empirical threshold of principal component selection is determined from subjective and objective aspects, that is, the range is 2.5, and the partial sill/sill is 0.2. According to the classification results based on the support vector machine algorithm, compared with traditional methods, the principal components with better image quality can be screened by using variable range and partial sill/sill, which can not only achieve the purpose of dimensionality reduction, but also ensure high classification accuracy.
Keywords hyperspectral      principal component analysis      spatial statistics      semi-variogram      support vector machine     
ZTFLH:  P962  
Corresponding Authors: PENG Junhuan     E-mail: sunxiao@mail.cgs.gov.cn;pengjunhuan@163.com
Issue Date: 20 June 2022
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Xiao SUN
Junhuan PENG
Feng ZHAO
Xiaoyang WANG
Jie LYU
Dengfeng ZHANG
Cite this article:   
Xiao SUN,Junhuan PENG,Feng ZHAO, et al. Principal component selection method for hyperspectral remote sensing images based on spatial statistics[J]. Remote Sensing for Natural Resources, 2022, 34(2): 37-46.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021214     OR     https://www.gtzyyg.com/EN/Y2022/V34/I2/37
Fig.1  Flowchart of algorithm
Fig.2  Simulation images (different grid sizes W=1,3,…,23; SNR=10,20,30,45)
Fig.3  Results of the range of the simulation image
Fig.4  Results of the partial sill/sill of the simulation image
Fig.5  Indian Pines data set
Fig.6  Pavia university data set
Fig.7  Salinas data set
Fig.8  The results of the semi-variogram parameters of real data
Fig.9  Thumbnails of each principal component after the PCA transformation of the Indian Pines data set
不同方法 筛选的主成分编号
PC(2~0.25) 1~8,10~21,23~25,28,38,66,68,88,92,101,108,109
PC(2~0.2) 1~8,10~25,28,29,38,55,59,66,88,92,101,103,108,109
PC(2.5~0.25) 1~8,10~21,23~25,28,38,66,68,88,92,101
PC(2.5~0.2) 1~8,10~25,28,29,38,59,66,68,88,92,101,103
Tab.1  Principal component selected by different thresholds in Indian Pines data set
数据集 PC
(2~0.25)		 PC
(2~0.2)		 PC
(2.5~0.25)		 PC
(2.5~0.2)
Indian Pines 0.912 0.912 0.905 0.906
Pavia U 0.856 0.881 0.858 0.883
Salinas 0.953 0.953 0.954 0.953
Tab.2  Kappa coefficient of different thresholds
Fig.10  Classification results of Indian Pines, Pavia U and Salinas data sets
数据集 PC
(0.99)		 PC
(10%)		 PC
(Entropy)		 PC
(2.5~0.2)
Indian Pines 0.864 0.853 0.899 0.906
Pavia U 0.718 0.884 0.884 0.883
Salinas 0.910 0.952 0.951 0.953
Tab.3  Kappa coefficient of different methods
Fig.11  Classification results of Indian Pines, Pavia U and Salinas data sets
地物种类 PC
(0.99)		 PC
(10%)		 PC
(Entropy)		 PC
(2.5~0.2)
塔楼 100 100 100 100
建筑物 74.02 75.2 81.45 79.53
林地 98.89 98.89 99.43 99.51
小麦地 100 100 100 100
大豆地 84.46 79.77 98.4 97.95
大豆略耕地 86.78 85.56 90.82 92.7
大豆未耕地 89.4 89.18 88.26 86.98
燕麦地 77.78 77.78 60 88.89
干草地 100 100 100 100
牧草已割地 81.82 90.91 94.12 100
草树地 100 99.77 100 100
牧草地 100 99.24 100 100
苜蓿地 85.71 80 86.84 85.71
玉米地 95.95 93.92 96.95 97.97
玉米未耕地 73.32 73.08 80.39 82.93
玉米略耕地 62.14 57.96 65.44 65.54
Tab.4  Prod. Acc of Indian Pines data set
地物种类 PC
(0.99)		 PC
(10%)		 PC
(Entropy)		 PC
(2.5~0.2)
草地 95.3 94.72 94.72 93.54
砂砾 42.87 72.63 72.63 72.00
金属板 100 99.91 99.91 99.82
裸地 31.51 79.64 79.64 81.02
柏油房顶 37.84 67.86 67.86 74.26
阴影 100 100 100 99.83
砖块 88.78 94.31 94.31 93.75
树 94.58 95.18 95.18 95.41
沥青路面 94.45 97.07 97.07 96.44
Tab.5  Prod. Acc of Pavia U data set
地物种类 PC
(0.99)		 PC
(10%)		 PC
(Entropy)		 PC
(2.5~0.2)
葡萄园垂直架子 98.23 99.92 99.84 99.83
未培育的葡萄园1 46.83 74.3 72.72 75.26
长叶莴苣_7wk 98.66 99.81 99.04 99.81
长叶莴苣_6wk 98.95 99.48 99.12 99.48
长叶莴苣_5wk 100 100 99.89 100
长叶莴苣_4wk 100 100 100 100
衰败的绿色杂草 99.35 100 100 100
生长中的葡萄园 100 100 100 100
未培育的葡萄园2 94.21 94.62 94.62 94.77
芹菜 100 100 100 100
作物残留 100 100 100 100
平整的休耕地 99.83 100 100 100
粗糙的休耕地 100 100 100 100
休耕地 100 100 100 100
椰菜2 99.95 100 100 100
椰菜1 99.49 99.91 100 100
Tab.6  Prod. Acc of Salinas data set
数据集 不同方法 筛选的主成分编号 个数
Indian Pines PC(0.99) 1~25 25
PC(10%) 1~20 20
PC(Entropy) 1~46,49 47
PC(2.5~0.2) 1~8,10~25,28,29,38,59,66,68,88,92,101,103 34
Pavia U PC(0.99) 1~4 4
PC(10%) 1-10 10
PC(Entropy) 1-10 10
PC(2.5~0.2) 1~10,16,19,23,24,43,71 16
Salinas PC(0.99) 1~3 3
PC(10%) 1~20 20
PC(Entropy) 1~100 100
PC(2.5~0.2) 1~10,12,15~23,25,28,29,34,42,61,66,75,77,118,126,134 32
Tab.7  Principal component information selected by different data sets
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