Modular eigen subspace scheme for high-dimensional data classification with NASA MODIS/ASTER (MASTER) airborne simulator data sets of Pacrim II project

Yang-Lang Chang; Chin-Chuan Han; Kuo-Chin Fan; Kun Shan Chen; Jeng-Horng Chang

doi:10.1117/12.451532

8 November 2002 Modular eigen subspace scheme for high-dimensional data classification with NASA MODIS/ASTER (MASTER) airborne simulator data sets of Pacrim II project

Yang-Lang Chang, Chin-Chuan Han, Kuo-Chin Fan, Kun Shan Chen, Jeng-Horng Chang

Author Affiliations +

Proceedings Volume 4816, Imaging Spectrometry VIII; (2002) https://doi.org/10.1117/12.451532
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

Abstract

In this paper, a novel filter-based greedy modular subspace (GMS)technique is proposed to improve the accuracy of high-dimensional remote sensing image supervisor classification. The approach initially divides the whole set of high-dimensional features into several arbitrary number of highly correlated subgroup by performing a greedy correlation matrix reordering transformation for each class. These GMS can be regarded as a unique feature for each distinguishable class in high-dimensional data sets. The similarity measures are next calculated by projecting the samples into different modular feature subspaces. Finally, a supervised multi-class classifer which is implemented based on positive Boolean function (PBF) schemes is adopted to build a non-linear optimal classifer. A PBF is exactly one sum-of-product form without any negative components. The PBF possesses the well-known threshold decomposition and stacking properties. The classification errors can be calculated from the summation of the absolute errors incurred at each level. The optimal PBF are found and designed as a classifer by minimize the classification error rate among the training samples. Experimental results demonstrate that the proposed GMS feature extraction method suits the PBF classifer best as a classification preprocess. It signifcantly improves the precision of image classification compared with conventional feature extraction schemes. Moreover, a practicable and convenient "vague" boundary sampling property of PBF is introduced to visually select training samples from high-dimensional data sets more effciently.

Citation Download Citation

Yang-Lang Chang, Chin-Chuan Han, Kuo-Chin Fan, Kun Shan Chen, and Jeng-Horng Chang "Modular eigen subspace scheme for high-dimensional data classification with NASA MODIS/ASTER (MASTER) airborne simulator data sets of Pacrim II project", Proc. SPIE 4816, Imaging Spectrometry VIII, (8 November 2002); https://doi.org/10.1117/12.451532

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