Minimum component eigen-vector based classification technique with application to TM images
نویسندگان
چکیده
In this paper, we propose a new classification technique based on the Minimum Component Analysis (MCA) instead of the traditional Principal Components Analysis (PCA). Most existing classification techniques based on PCA represent a class by its principal component. However, the principal component is not always the best choice since there is a high possibility for classes to overlap with each other in the principal component direction. The new minimum component eigen-vector based classification technique overcomes this disadvantage by representing a class with its minimum component. In addition, a minimum likelihood decision rule is employed instead of maximum likelihood decision rule. Good performance of our technique is verified by experimental results on Kennedy Space Center (KSC) TM images.
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