Kernel-based joint spectral and spatial exploitation using Hilbert space embedding for hyperspectral classification

Prudhvi Gurram; Heesung Kwon

doi:10.1117/12.918338

24 May 2012 Kernel-based joint spectral and spatial exploitation using Hilbert space embedding for hyperspectral classification

Prudhvi Gurram, Heesung Kwon

Author Affiliations +

Proceedings Volume 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII; 83901R (2012) https://doi.org/10.1117/12.918338
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

In this paper, a Support Vector Machine (SVM) based method to jointly exploit spectral and spatial information from hyperspectral images to improve classication performance is presented. In order to optimally exploit this joint information, we propose to use a novel idea of embedding a local distribution of input hyperspectral data into the Reproducing Kernel Hilbert Spaces (RKHS). A Hilbert Space Embedding called mean map is utilized to map a group of neighboring pixels of a hyperspectral image into the RKHS and then, calculate the empirical mean of the mapped points in the RKHS. SVM based classication performed on the mean mapped points can fully exploit the spectral information as well as ensure spatial continuity among neighboring pixels. The proposed technique showed signicant improvement over the existing composite kernels on two hyperspectral image data sets.

Citation Download Citation

Prudhvi Gurram and Heesung Kwon "Kernel-based joint spectral and spatial exploitation using Hilbert space embedding for hyperspectral classification", Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901R (24 May 2012); https://doi.org/10.1117/12.918338

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