Journal of Jilin University(Earth Science Edition) ›› 2015, Vol. 45 ›› Issue (4): 1246-1256.doi: 10.13278/j.cnki.jjuese.201504304

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Remote Sensing Classification Information Extraction Based on Rough Set Theory

Zhou Linfei, Chen Qixin, Cheng Qian, Zhang Jing   

  1. College of Water Conservancy, Shenyang Agricultural University, Shenyang 110161, China
  • Received:2014-10-21 Published:2015-07-26

Abstract:

Using Shuangtaizi estuarine wetland as the research area, and Landsat 8 and HJ-1-A/HJ-1-B remote sensing data as the data sources, this study was conducted for land-cover information extraction. According to the status of the land, the study area was divided into 9 categories, including upland, reed, paddy field, Suaeda, mixed vegetation, water body, beach, residential land, culture pond. First, the study area was divided into vegetation and non-vegetation using the time-series normalized difference vegetation index (NDVI). Then, the classification rules of vegetation and non-vegetation were extracted based on the rough set theory and on multi-temporal remote sensing data. Finally, a decision tree classification model was established. For the purpose of an accurate evaluation, the maximum likelihood classification was conducted based on the pixels using the same training samples; and the confusion matrix and kappa coefficient were calculated. The results showed that the overall accuracy both of the deeision tree and the maximum likelihood classification reached up to 93.70% and 91.62% with a kappa coefficient of 0.92 and 0.90 respectively. The two evaluation index values were improved. It provided a novel research idea for a wetland classification information extraction based on remote sensing images.

Key words: Shuangtaizi estuarine wetlands, remote sensing classification, normalized difference vegetation index (NDVI), rough set theory, decision tree

CLC Number: 

  • TP79

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