سابِق|تال

Automatic collection for land cover classification based on multisource datasets

  • role： First author第一作者
  • Affiliation：

    College of Environment and Planning, Henan University, Kaifeng 475004, China

    College of Computer and Information Engineering, Henan University, Kaifeng 475004, China

  • Email：hyb@henu.edu.cn
  • Introduction：黄亚博(1982—　)，女，讲师，研究方向为遥感地学应用。E-mail：hyb@henu.edu.cn
HUANG Yabo13,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Environment and Planning, Henan University, Kaifeng 475004, China

    Institute of Disaster Prevention, Beijing 101601, China

    Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China

  • Email：liaosb@igsnrr.ac.cn
  • Introduction：廖顺宝(1966—　)，男，教授，研究方向为遥感与GIS应用及地学信息产品加工与质量评价等。E-mail：liaosb@igsnrr.ac.cn
LIAO Shunbao124*

ملخص

The capability of remotely sensed data acquisition is constantly improved. Thus, enhancing the automation degree for land cover classification at a large scale by remote sensing has become a key problem. However, present manual methods of selecting samples are becoming the bottleneck of automatic land cover classification. Many global and national land cover datasets based on remote sensing have been produced in the past two decades for different international or national initiatives. However, the rich knowledge implied in these products has not been fully exploited. The overall objective of this study is to set up an automatic land cover classification approach at a large scale by remote sensing through an automatic method of collecting land cover samples based on multisource datasets. The practical goals are to improve automation degree of land cover classification and enhance the accuracy of land cover classification. Henan and Guizhou provinces were selected as the study areas based on their types of land covers. First, the national land use database of China at a scale of 1∶100000 (CHINALC) and global land cover data (GlobleLand30) at a resolution of 30 m were selected as the data sources for the sample collection. Second, the initial sample areas were collected based on the spatial consistency analysis and heterogeneity analysis. Third, invalid samples were removed from the initial samples through the technology of sample purification. Finally, the Jeffries–Matusita distance was used to measure the classification feature separability of the samples between the different land cover types to prove the feasibility of the proposed method. The accuracy of the land cover product by the proposed method of sample collection was assessed and compared with the globe land cover product MCD12Q1. Experimental results show that the following: (a) The overall accuracy of the classification product through the proposed automatic method of sample collection based on multisource datasets was higher than that of the global land cover product MCD12Q1, which was classified based on the manual method of sample selection. The overall accuracy of the land cover classification product based on the proposed method was 78% in Henan province and 57% in Guizhou province, whereas that of MCD12Q1 was 74% and 55%, respectively. The Kappa coefficients of the former were 0.54 and 0.25, respectively, whereas those of the latter were 0.42 and 0.15, respectively. (b) Compared with the method of sample collection based on a single source land cover dataset, the proposed automatic method of sample collection based on multisource datasets had better classification stability and higher classification feature separability. The standard deviation of the Kappa coefficient and accuracy of products by 8-time experiments were less than 0.004. The classified results were also more stable. Unlike the method of sample collection based on a single source land cover dataset, the proposed automatic method of land cover sample collection based on multisource datasets not only improves the automation degree of land cover classification, but also enhances the accuracy of land cover classification.

مفهوم

automation;samples collection;land-cover/land-use;classification;MODIS

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