The degradation of alpine cold meadow in the source region of the Yangtze River and Yellow River has been causing very serious ecological and social economic problems. To study degradation of alpine cold meadow in the headwater region from 1994 to 2006, Remote Sensing and the machine learning approach was used. Firstly, a classification system, considering the dynamic evolution process of alpine meadow, was offered. Secondly, the support vector machines approach was introduced into classification of satellite remote sensing images. The experiment results show that, the introduced classification system can express dynamic process of alpine cold meadow degradation efficiently; SVM has obtained better precision than minimum distance and maximum likelihood methods for TM images interpretation. Furthermore, the research indicates that degradation of the alpine cold meadow was most marked from 1994 to 2006, and the area of BS&BR land has expanded rapidly.
To solve the problem of interposition between trees or trees and between trees and ground in forest images which would bring on error-matching and be unable to construct a full 3D-network, a new approach for segmentation of forest images was proposed. The proposed color divergence was defined over the index class map by quantized image, which was a good indicator of whether that area was in region center or near region boundaries. Using this measure, image texture was analyzed by multi-resolution. Then the initial over-segmented regions were merged according to Laws texture energy measure. Experimental demonstrated that the segmentation results of forest images on the proposed approach hold favorable consistency in terms of human perception. The classification accuracy was 80%. The recognized trees and ground can offer dependable data for image matching and 3D modeling.
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