Tree Species Identification Using XGBoost Based on GF-2 Images

doi:10.13466/j.cnki.lyzygl.2019.05.009

Abstract

Abstract:

Tree species distribution is an important indicator of forest resources monitoring,and it is also one of the difficulties in the application of remote sensing images in forest resources monitoring.Based on domestic GF-2 satellite imagery data,forest resources survey data and DEM data,combined with spectral,texture,index and topographic factors and other characteristics.Three classification algorithms,support vector machine,random forest and XGBoost,are compared,and the optimal algorithm (XGBoost) is selected according to classification accuracy.Four dominant tree species,broad-leaved trees,Pinus massoniana,Chinese fir and Phyllostachys pubescens,were classified by row feature selection.The results show that the total classification accuracy of XGBoost classification model is 83.58% and the Kappa coefficient is 0.77,which is significantly higher than that of support vector machine and random forest classification method.After feature selection,the classification accuracy of tree species has not been improved obviously,but the redundancy of features can be reduced,which provides a reference for feature selection and dimensionality reduction in small sample data.

Key words: GF-2, feature selection, XGBoost, tree species identification, remote sensing

CLC Number:

TP79
S757.2

CAI Linfei, WU Dasheng, FANG Luming, ZHENG Xinyu. Tree Species Identification Using XGBoost Based on GF-2 Images[J]. FOREST RESOURCES WANAGEMENT, 2019, (5): 44-51.

Figures/Tables 10

Tab.1

Tab.2

Selected vegetation index characteristics

指数名称	简写	公式	出处
归一化植被指数	NDVI	$ρ NIR - ρ R ρ NIR + ρ R$	[11]
土壤调整植被指数	SAVI	$ρ NIR - ρ R) × 1.5 (ρ NIR + ρ R) × 0.5$	[12]
差值植被指数	DVI	$ρ NIR - ρ R$	[13]
增强型植被指数	EVI	$2.5 (ρ NIR - ρ R) ρ NIR + 6.0 ρ R - 7.5 ρ B + 1$	[14]
归一化水指数	NDWI	$ρ G - ρ NIR ρ G + ρ NIR$	[15]
类胡萝卜素指数	CRI	$1 ρ B - 1 ρ G$	[16]
非线性植被指数	NLI	$ρ NIR 2 - ρ R ρ NIR 2 + ρ R$	[16]
归一化差异绿度指数	NDGI	$ρ G - ρ R ρ G + ρ R$	[17]

Tab.2

Tab.3

Gray level co-occurrence matrix texture measuring table

序号	纹理测度	公式	描述
1	同质性(Homogeneity)	$∑ i = 0, j = 0 N - 1 P ij × i 1 + (i - j) 2$	反映局部纹理变化大小
2	熵(Entropy)	$∑ i = 0, j = 0 N - 1 i × P ij (- ln P ij)$	信息量的度量,纹理越复杂,值越大
3	非相似性(Dissimilarity)	$∑ i = 0, j = 0 N - 1 i × P ij \| i - j \|$	局部对比度越高,则非相似性也越高
4	均值(Mean)	$∑ i = 0, j = 0 N - 1 i × P ij$	反映纹理规则程度,与均值呈正相关
5	对比度(Contrast)	$∑ i = 0, j = 0 N - 1 i × P ij (i - j) 2$	反映图像均匀性,对非均匀灰度区域敏感,灰度变化越大值越小
6	相关性(Correlation)	$∑ i = 0, j = 0 N - 1 i × P ij (i - M E) × (j - M E) V A i × V A j$	度量相邻区域相关性,相关性越高,值越大
7	二阶矩(Second Moment)	$∑ i = 0, j = 0 N - 1 i × P ij 2$	反映图像纹理变化、分布的均一性程度
8	方差/协方差(Variance)	$∑ i = 0, j = 0 N - 1 i × P ij (i - M E) 2$	反映像素值和均值偏差的度量,当灰度变化较大时,值较大

Tab.3

Tab.4

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Tab.5

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卫星	通道	波段	光谱范围/μm	空间分辨率/m	幅宽/km
	Green	B1	0.45~0.52	4
	Red	B2	0.52~0.59	4
GF-2	Blue	B3	0.63~0.69	4	45
	Nir	B4	0.77~0.89	4
	PAN	P	0.45~9.90	1

类别	SVM		RF		XGBoost
类别	UA	PA	UA	PA	UA	PA
阔叶树	70.51	67.9	74.36	69.88	80.77	73.26
马尾松	58.33	73.68	62.5	83.33	70.83	89.47
杉木	82	70.16	87.74	78.15	86.79	82.14
毛竹	84.47	94.57	87.38	92.78	88.35	91.92
OA/%	76.72		80.9		83.58
Kappa	0.68		0.74		0.77

类别	阔叶树	马尾松	杉木	毛竹	用户精度/%
阔叶树	62	3	9	4	79.49
马尾松	6	33	9	0	68.75
杉木	8	1	92	5	86.79
毛竹	5	1	3	94	91.26
生产者精度/%	76.54	86.84	81.42	91.26
总体精度/%			83.88
Kappa			0.78