International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 641–648, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-641-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 641–648, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-641-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Feb 2020

07 Feb 2020

STUDY ON THE WATER BODY EXTRACTION USING GF-1 DATA BASED ON ADABOOST INTEGRATED LEARNING ALGORITHM

J. Y. Sun1,2, G. Z. Wang2, G. J. He2, D. C. Pu1,2, W. Jiang2, T. T. Li1, and X. F. Niu1 J. Y. Sun et al.
  • 1College of Geo-exploration Science and Technology, Jilin University, Changchun,130026, China
  • 2Aerospace Information Research Institute, Chinese Academy of Science, Beijing, 100094, China

Keywords: Integrated Learning Algorithm, GF-1, Water Body Extraction, Adaboost, Random Forest, Sampling Strategy

Abstract. Surface water system is an important part of global ecosystem, and the changes in surface water may lead to disasters, such as drought, waterlog, and water-borne diseases. The rapid development of remote sensing technology has supplied better strategies for water bodies extraction and further monitoring. In this study, AdaBoost and Random Forest (RF), two typical algorithms in integrated learning, were applied to extract water bodies in Chaozhou area (mainly located in Guangzhou Province, China) based on GF-1 data, and the Decision Tree (DT) was used for comparative tests to comprehensively evaluate the performance of classification algorithms listed above for surface water body extraction. The results showed that: (1) Compared with visual interpretation, AdaBoost performed better than RF in the extraction of several typical water bodies, such as rivers, lakes and ponds Moreover, the water extraction results of the strong classifiers using AdaBoost or RF were better than the weak basic classifiers. (2) For the quantitative accuracy statistics, the overall accuracy (96.5%) and kappa coefficient (93%) using AdaBoost exceeded those using RF (5.3% and 10.6%), respectively. The classification time of AdaBoost increased by 403 seconds and 918 seconds relative to RF and DT methods. However, in terms of visual interpretation, quantitative statistical accuracy and classification time, AdaBoost algorithm was more suitable for the water body extraction. (3) For the sample proportion comparison experiment of AdaBoost, four sampling proportions (0.1%, 0.2%, 1% and 2%) were chosen and 0.1% sampling proportion reached the optimum classification accuracy (93.9%) and kappa coefficient (87.8%).