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Data Mining for Forest Space Information Features from Remote Sensing Images

Author: QinLiMei
Tutor: WangXiuXin
School: Guangxi Normal University
Course: Pattern Recognition and Intelligent Systems
Keywords: Water and forest resources Remote Sensing Image Data Mining Texture WNN Feature identification
CLC: TP751
Type: Master's thesis
Year: 2011
Downloads: 70
Quote: 2
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Abstract


Increasingly serious global environmental destruction, drought, dust storms, floods and other natural disasters occur frequently a serious impact on people's lives, production, economic development and social stability. To mitigate these natural disasters, strengthen water conservation forest protection and research has become extremely important. Water forest cover a large range of traditional methods of observation points are difficult to make a comprehensive analysis of remote sensing for the effective analysis of water conservation forests planar information provides a means. In recent years, data mining, information industry caused a great deal of attention, a large amount of data into useful information and knowledge is our urgent needs. Remote sensing image contains a lot of information, it became the most potential in data mining field. Multispectral remote sensing study area image data mining, a wavelet analysis theory and artificial neural network to distinguish between spectral characteristics similar to woodland trees and field crops, levels of grayscale images for remote sensing rich enough, is not conducive to observation and analysis, can not be fully effective use of multi-band satellite information, this weak utilization issues and \Classification by ratio vegetation index and normalized difference vegetation index analysis water forest change; vegetation cover and surface types on surface temperature impact analysis; research results show that wavelet BP neural network can effectively identify the spectrum similar woodland trees and field crops, wavelet texture feature identifying BP neural network classification method can significantly improve the \sunny woodland rate reached 89.1 percent, up 88.6 percent shady woodland, farmland reached 87.8%, bare soil rock and water 98.9%, and the overall false acceptance rate of 6.39%, better than rely solely on spectral information maximum likelihood method and BP neural network method for remote sensing image hilly complex terrain sunny and shady distinction, can identify more classification rules for rationality, to improve the recognition accuracy of classification. Vegetation index and vegetation coverage for the analysis of the Lijiang River water upstream forest cover change provides an effective means. Research innovation lies: we propose a new wavelet analysis theory, neural network theory, select the best bands of remote sensing images, optimal wavelet to extract spectral features of wavelet and wavelet texture feature, the use of BP neural network to identify surface features classification method. This method of ensuring correct classification rate of recognition under the premise of improving the utilization of remote sensing grayscale image, reducing cost and computational complexity classification.

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CLC: > Industrial Technology > Automation technology,computer technology > Remote sensing technology > Interpretation, identification and processing of remote sensing images > Image processing methods
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