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The Remote Sensing Monitoring Analysis Based on Object-Oriented Classification Method


作者


摘要

In this paper, based on multi-temporal remote monitoring technology, using object-oriented classification method to monitor the change of vegetation of Zhangye oasis from TM/ETM data in 1989, 2000, 2011 years. The results show that: (1) the multi-resolution segmentation converted the single cell which had the similar texture, spectrum and shape to the object. Integrating nearest neighbor classifier and membership classifier to class the three data, and the overall accuracy of classification was 89.5%, Kappa coefficient was 0.9. The classification stability was 0.45 and 0.47 in 2000 and 2011 years. It showed that object-oriented classification method accuracy is higher than traditional classification method. (2)The three classification results indicate the area of bare land was larger than other, and it was reducing, with a percentage was 73.21%, 64.76%, 60.17%. The vegetation mainly distributed in both sides of Heihe River, the percentage of three data were 16.01%, 29.9%, 33.6%. The saline land was mainly distributed in the northwest of the oasis region, the percentage dropped to 2.33% from 4.89% during 1989 and 2011 years. (3) NDVI of the upstream was higher than the NDVI of the downstream on sides of river, the NDVI raised and the maximum value was 0.54. NDVI increased significantly from 1989 to 2011 years in Linze central region, and the maximum value reached to 0.58 in 2011 years, and it had the same characteristic in the southeast of Ganzhou district. The average NDVI of 2011 years was higher than in 2000 and 1989.


关键词

  • Accuracy Assessment
  • Computer Graphics
  • Computer Imaging, Vision, Pattern Recognition and Graphics
  • Hexi Corridor
  • Image Processing and Computer Vision
  • Multi-resolution Segmentation
  • Object-oriented Classification
  • Pattern Recognition
  • remote sensing monitoring

引用方式

Wang H, Dai S, Huang X B. The Remote Sensing Monitoring Analysis Based on Object-Oriented Classification Method[M]. Advances in Image and Graphics Technologies, Springer, 2013, 92-101.

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