Land use and land cover classification using Chinese GF-2 multispectral data in a region of the North China Plain

doi:10.1007/s11707-018-0734-8

Front. Earth Sci.

2019, Vol. 13

Issue (2) : 327-335 https://doi.org/10.1007/s11707-018-0734-8

RESEARCH ARTICLE

Land use and land cover classification using Chinese GF-2 multispectral data in a region of the North China Plain

Kun JIA^1,², Jingcan LIU^1,², Yixuan TU^1,², Qiangzi LI³(

), Zhiwei SUN⁴, Xiangqin WEI³, Yunjun YAO^1,², Xiaotong ZHANG^1,²

¹. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China
². Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
³. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
⁴. Beijing Geoway Times Software Technology Co., Ltd., Beijing 100043, China

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Abstract

The newly launched GF-2 satellite is now the most advanced civil satellite in China to collect high spatial resolution remote sensing data. This study investigated the capability and strategy of GF-2 multispectral data for land use and land cover (LULC) classification in a region of the North China Plain. The pixel-based and object-based classifications using maximum likelihood (MLC) and support vector machine (SVM) classifiers were evaluated to determine the classification strategy that was suitable for GF-2 multispectral data. The validation results indicated that GF-2 multispectral data achieved satisfactory LULC classification performance, and object-based classification using the SVM classifier achieved the best classification accuracy with an overall classification accuracy of 94.33% and kappa coefficient of 0.911. Therefore, considering the LULC classification performance and data characteristics, GF-2 satellite data could serve as a valuable and reliable high-resolution data source for land surface monitoring. Future works should focus on improving LULC classification accuracy by exploring more classification features and exploring the potential applications of GF-2 data in related applications.

Keywords land use and land cover classification GF-2 North China Plain multispectral data

Corresponding Author(s): Qiangzi LI

Just Accepted Date: 29 November 2018 Online First Date: 25 December 2018 Issue Date: 16 May 2019

Cite this article:

Kun JIA,Jingcan LIU,Yixuan TU, et al. Land use and land cover classification using Chinese GF-2 multispectral data in a region of the North China Plain[J]. Front. Earth Sci., 2019, 13(2): 327-335.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0734-8
https://academic.hep.com.cn/fesci/EN/Y2019/V13/I2/327

Tab.1 The main technical specifications of GF-2 multispectral cameras

Fig.1 The geographical location of the study area in the North China Plain. The image is the standard false color composited image (R: NIR, G: red, B: green) of GF-2 multispectral data acquired on April 5, 2016.

Fig.2 Pixel-based and object-based LULC classification results of the GF-2 multispectral data using MLC and SVM classifiers, respectively (P-MLC: pixel-based classification using the MLC classifier, P-SVM: pixel-based classification using the SVM classifier, O-MLC: object-based classification using the MLC classifier, O-SVM: object-based classification using the SVM classifier).

Fig.3 A typical enlarged region (centered at 37°8′46″N, 117°24′6″E) to show the differences in the LULC classification results from different classification strategies (P-MLC: pixel-based classification using the MLC classifier, P-SVM: pixel-based classification using the SVM classifier, O-MLC: object-based classification using the MLC classifier, O-SVM: object-based classification using the SVM classifier).

Tab.2 Confusion matrix for LULC classification of the GF-2 multispectral data using the pixel-based classification strategy and the MLC classifier. (OA: 86.67%, kappa coefficient: 0.796)

Tab.3 Confusion matrix for LULC classification of the GF-2 multispectral data using the pixel-based classification strategy and the SVM classifier (OA: 89.30%, kappa coefficient: 0.836)

Tab.4 Confusion matrix for LULC classification of the GF-2 multispectral data using the object-based classification strategy and the MLC classifier (OA: 91.57%, kappa coefficient: 0.870)

Tab.5 Confusion matrix for LULC classification of the GF-2 multispectral data using the object-based classification strategy and the SVM classifier (OA: 94.33%, kappa coefficient: 0.911)

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