Multiscale filter-based hyperspectral image classification with PCA and SVM

Abstract

Hyperspectral imagery can offer images with high spectral resolution and provide a unique ability to distinguish the subtle spectral signatures of different land covers. In this paper, we develop a new algorithm for hyperspectral image classification by using principal component analysis (PCA) and support vector machines (SVM). We use PCA to reduce the dimensionality of an HSI data cube, and then perform spatial convolution with three different filters on the PCA output cube. We feed all three convolved output cubes to SVM to classify every pixel. Finally, we perform fusion on the three output maps to determine the final classification map. We conduct experiments on three widely used hyperspectral image data cubes (ie indian pines, pavia university, and salinas). Our method can improve the classification accuracy significantly when compared to several existing methods. Our novel method is relatively fast in term of CPU computational time as well.

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

“…PCA is one of the best preprocessing methods considered to date for improvised spectral dimension reduction [ 180 ], proper selection of spectral bands and their multiscale features in a segmented format [ 181 , 199 ], noise-reduced spectral analysis [ 27 ], and feature extraction [ 130 , 196 ]. PCA, in collaboration with SVM [ 195 , 200 ], DL for feature reduction and better classification [ 182 , 183 ], CNN with multiscale feature extraction [ 188 , 189 ], and sparse tensor technology [ 190 ], has highly been appreciated as soulful research. All these recent time collaborations and a special honor to the merging of ICA-DCT with CNN cited in [ 191 ] are the evidence that although PCA is categorized under traditional methods, it is supremely relevant for its significant usefulness in handling HSIs.…”