Feature extraction is the most critical step in classification of multispectral *** classification accuracy is mainly influenced by the feature sets that are selected to classify the *** the past,handcrafted feature s...
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Feature extraction is the most critical step in classification of multispectral *** classification accuracy is mainly influenced by the feature sets that are selected to classify the *** the past,handcrafted feature sets are used which are not adaptive for different image *** overcome this,an evolu-tionary learning method is developed to automatically learn the spatial-spectral features for classification.A modified Fireflyalgorithm(FA)which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this *** extracting the most effi-cient features from the data set,we have used 3-D discrete wavelet transform which decompose the multispectral image in all three *** selecting spatial and spectral features we have studied three different approaches namely overlapping window(OW-3DFS),non-overlapping window(NW-3DFS)adaptive window cube(AW-3DFS)and Pixel based *** Multiclass Support Vector Machine(MSVM)is used for classification *** con-ducted on Madurai LISS IV multispectral image exploited that the adaptive win-dow approach is used to increase the classification accuracy.
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