Significance of Eigen Matrix in Spectral Domain of Remote Sensing Images (RSI)
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10.5120/4434-6174 |
S Murugan, Dr. Jothi C Venkateswaran and Dr. N Radhakrishnan. Article: Significance of Eigen Matrix in Spectral Domain of Remote Sensing Images (RSI). International Journal of Computer Applications 35(10):1-5, December 2011. Full text available. BibTeX
@article{key:article,
author = {S. Murugan and Dr. C. Jothi Venkateswaran and Dr. N. Radhakrishnan},
title = {Article: Significance of Eigen Matrix in Spectral Domain of Remote Sensing Images (RSI)},
journal = {International Journal of Computer Applications},
year = {2011},
volume = {35},
number = {10},
pages = {1-5},
month = {December},
note = {Full text available}
}
Abstract
Information extraction from RSI involves a significant level of testing and experimentation before arriving at an acceptable solution. It includes combination of techniques that hardly have clear cut rules except generating desired output with acceptable level of accuracy. This may contain many levels of mining techniques depending upon the level of information required, time and system efficiency. The first level of image mining may be involving some primitive operations to reduce noise, enhancement and filtering in RSI domain. Secondly, the process may involve image segmentation and recognition of features. Finally, the image mining could involve cognitive analysis and extraction of features from RSI. Another important factor about RSI is its multiband information about objects that require a more complicated procedure even at the preprocessing level. The multilayered RSI data may be reduced to a single band data without losing much information by using Eigen values. The output PCA image thus derived may help in identifying prominent features and encourage further extension towards cognitive information extraction process.
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