Greedy modular subspace segment principle component analysis

Hsin-Ting Chen; Hsuan Ren; Yang-Lang Chang

doi:10.1117/12.739230

25 September 2007 Greedy modular subspace segment principle component analysis

Hsin-Ting Chen, Hsuan Ren, Yang-Lang Chang

Proceedings Volume 6756, Chemical and Biological Sensors for Industrial and Environmental Monitoring III; 67560F (2007) https://doi.org/10.1117/12.739230
Event: Optics East, 2007, Boston, MA, United States

Abstract

Hyperspectral images collect hundreds of co-registered images of the earth surface with different wavelengths in visible and short-wave inferred region. With such high spectral resolution, many adjacent bands are highly correlated, i.e., they contain a lot redundant information. How to remove unnecessary information from this huge amount of data and preserve all the information is a challenging problem. Principal component analysis (PCA) is one of the widely used algorithms for this problem. It assumes the larger variance contains the most information, so it projects the data into the direction to maximize the variance. Most of the signals will be kept in the first several principal components, and the rest will be considered to be noise and neglected. To further reduce the redundancy, segment PCA is proposed, which first separate the whole spectral bands into blocks and then perform the original PCA in each block individually. Both these two approaches perform well for data compression, but for image classification in its feature space, they did not achieve comparable results. In this study, we adopt the greedy modular subspaces transformation (GMST) to find the optimal feature subspace for the segment PCA. It is expected to provide a comparable classification results with high compression performance.

Citation Download Citation

Hsin-Ting Chen, Hsuan Ren, and Yang-Lang Chang "Greedy modular subspace segment principle component analysis", Proc. SPIE 6756, Chemical and Biological Sensors for Industrial and Environmental Monitoring III, 67560F (25 September 2007); https://doi.org/10.1117/12.739230

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Principal component analysis

Image segmentation

Feature extraction

Vegetation

Hyperspectral imaging

Spectral resolution

Visible radiation

Show All Keywords

Keywords/Phrases

Search In:

Publication Years