Bulletin of Surveying and Mapping ›› 2019, Vol. 0 ›› Issue (11): 8-11,43.doi: 10.13474/j.cnki.11-2246.2019.0342

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Review and prospect of land cover mapping by remote sensing

ZHU Shuang1, ZHANG Jinshui2,3,4, LI Changqing1, ZHENG Kuo1   

  1. 1. Beijing Polytechnic College, Beijing 100042, China;
    2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;
    3. Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;
    4. Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • Received:2019-02-15 Revised:2019-04-07 Published:2019-12-02

Abstract: The study of land use/cover classification at regional scale is one of the important fields in the study of land use/cover change in the world. Accurate and timely acquisition of the characteristics of the earth's surface is essential to grasp the relationship and interaction between human and natural phenomena. Based on the characteristics of land cover remote sensing classification methods, this paper summarizes the research results here and abroad from hard classification methods, soft classification methods and the latest soft and hard classification methods, and analyses the classification strategies, characteristics and applicability of various methods. The results show that the soft and hard classification method can be flexibly applied to the characteristics of remote sensing images, such as the coexistence of pure and mixed pixels, and can effectively solve the spectral heterogeneity. It has broad application potential in remote sensing monitoring of land cover. In this paper, a framework of soft and hard classification land cover mapping method based on variable endpoints is proposed, and the research emphasis in the future is pointed out.

Key words: hard classification method, soft classification method, soft and hard classification method, mixed pixel decomposition with dynamic endmember

CLC Number: