Share Email Print

Proceedings Paper

Kernel-based joint spectral and spatial exploitation using Hilbert space embedding for hyperspectral classification
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 24 May 2012
PDF: 7 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901R (24 May 2012); doi: 10.1117/12.918338
Show Author Affiliations
Prudhvi Gurram, U.S. Army Research Lab. (United States)
Heesung Kwon, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?