By Topic

A Distributed Approach to Hyper-Spectral Image Analysis Using Support Vectors

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
S. Sindhumol ; Department of Information Technology, Avenir Computer Services Export Pvt. Ltd, Kochi, Kerala, sindhumol ; M. Wilscy

This paper presents a detailed analysis of a new distributed algorithm designed for hyper-spectral image analysis. Hyper-spectral imaging is a valuable technique for detection and classification of materials and objects on the Earth's surface. The conventional approach to hyper-spectral image analysis is based on dimensionality reduction using Principal Component Analysis (PCA). But the results contain more details of the frequently occurred objects compared to the minor objects in the scene. To resolve this, a new algorithm for hyper-spectral image analysis based on Support Vector Clustering (SVC) and Spectral Angle Mapping (SAM) is proposed in this work. The method is found to generate good results, but the calculation of Support Vectors, Spectral Angles and Principal Components are very time-consuming processes and a bulk of data is to be processed to analyse the hyper-spectral images. So the algorithm is designed in a distributed manner and a distributed environment based on Java/RMI is developed to implement it. The algorithm is tested with two Hyper-spectral image datasets of 210 bands each, which are taken with HYper-spectral Digital Imagery Collection Experiment (HYDICE) air-borne sensors. A performance analysis of the distributed environment is also carried out.

Published in:

Intelligent Sensing and Information Processing, 2006. ICISIP 2006. Fourth International Conference on

Date of Conference:

Oct. 15 2006-Dec. 18 2006