18 October 2005 A comparison of statistical and multiresolution texture features for improving hyperspectral image classification
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Abstract
This paper presents a method for combining both spectral and spatial features to perform hyperspectral image classification. Texture based spatial features computed from statistical, wavelet multiresolution, Fourier spectrum and Gabor filters are considered. A step wise feature selection method selects optimal set of features from the combined feature set. A comparison of the different spatial features for improving hyperspectral image classification is presented. The results show that wavelet based features and statistical features perform best. The effect of band subset selection using information based subset selection methods on the combined feature set is presented. Several results with hyperspectral images show the efficacy of utilizing spatial features.
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Vidya Manian, Luis O. Jimenez-Rodriguez, Miguel Velez-Reyes, "A comparison of statistical and multiresolution texture features for improving hyperspectral image classification", Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820I (18 October 2005); doi: 10.1117/12.627634; https://doi.org/10.1117/12.627634
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