Application and analyses of texture features based on GF-1 WFV images in monthly information extraction of crops

doi:10.6046/zrzyyg.2020334

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Abstract

The crop planting structure consists of information such as crop species, quantity structure, and spatial distribution characteristics, and it serves as the basis for agricultural scientific management. Taking the Shijin irrigation area, Hebei Province as the study area and on the premise of not considering the optimal window period of crop time series, this study calculates and analyzes the ability of texture features in crop classification and identification based on GF-1WFV images. Meanwhile, the vegetation index is introduced into the time phase in which the classification effects based on texture features are poor, in order to make up for the defects of texture in the expression of crops. According to the comparison of the classification results of various groups, the classification accuracy of individual texture features reached greater than 80% in April and August when the crop structure is obvious but was still less than 80% in May, June, July, and September when crops are the most complex. After combining the texture features with the vegetation index, the classification results of the crops in these four months were greatly improved. In detail, the overall classification accuracy was greater than 80%, which basically meets the need for agricultural dynamic monitoring. Meanwhile, the accuracy was improved by 2.27%~9.75 % and the Kappa coefficient was increased by 0.02~0.16 compared to the individual texture features. As verified using summer maize samples, the recognition accuracy reached up to 98%, the recognition effects were relatively complete, the fragmentation degree was the minimum, and the optimal discrimination from other crop categories was achieved. Meanwhile, it also proved that the texture features based on GF-1WFV images can be applied to the extraction of the crop planting structure, especially in the months when the crop structure is relatively obvious, and they can provide some effective information for the information extraction of crops base on images.

Keywords GF-1 WFV crop planting structure classification texture accuracy

ZTFLH:

TP79

Corresponding Authors: ZHAO Hongli E-mail: 942437026@qq.com;zhaohl@iwhr.com

Issue Date: 24 September 2021

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	Rong WANG
	Hongli ZHAO
	Yunzhong JIANG
	Yi HE
	Hao DUAN

Cite this article:

Rong WANG,Hongli ZHAO,Yunzhong JIANG, et al. Application and analyses of texture features based on GF-1 WFV images in monthly information extraction of crops[J]. Remote Sensing for Natural Resources, 2021, 33(3): 72-79.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020334 OR https://www.gtzyyg.com/EN/Y2021/V33/I3/72

Fig.1 The location of research area and the spectral characteristics of sample

Fig.2 Location of sampling points in study area

统计量	计算公式
Mean(平均值)	- $∑ i = 0 L - 1 ∑ i = 0 L - 1$ i×p(i,j)
Var(方差)	$∑ i = 0 L - 1 ∑ i = 0 L - 1$ (i-μ)²×p(i,j)
Con(对比度)	$∑ i = 0 L - 1$ n $∑ i = 0 L - 1 ∑ i = 0 L - 1 i × p (i, j)$
Hom(同质性)	$∑ i = 0 L - 1 ∑ i = 0 L - 1 p (i, j) 1 + (i - j) 2$
Dis (非相似性)	$∑ i = 0 L - 1 ∑ i = 0 L - 1 i - j$ ×p(i,j)
Ent(信息熵)	- $∑ i = 0 L ∑ i = 0 L$ p(i,j)×lgp(i,j)
ASM(二阶矩)	$∑ i = 0 L ∑ i = 0 L (p (i, j)) 2$
Cor(相关性)	$∑ i = 0 L ∑ i = 0 L (ij) p (i, j) - u i u j s i s j$

Tab.1 Texture feature

Tab.2 The statistic of texture(km²)

Fig.3 The result of crop classification based on texture

Tab.3 The statistic of combination(km²)

Tab.4 Classification accuracy evaluation

Fig.4 The classification results about experiments

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