A rough sets approach of hyperspectral image classification

Zhaocong Wu; Deren Li

doi:10.1117/12.654876

3 November 2005 A rough sets approach of hyperspectral image classification

Zhaocong Wu, Deren Li

Proceedings Volume 6043, MIPPR 2005: SAR and Multispectral Image Processing; 604310 (2005) https://doi.org/10.1117/12.654876
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

Rough set theory has a powerful capability for attributes reduction and classification rules extraction, while artificial neural network (ANN) performances well in classification problems with a satisfactory accuracy. In this paper we focus our attention to investigate a way of integrating rough set theory and multi layer perceptron (MLP) in soft computing paradigm for classification and rule generation of hyperspectral remote sensing image classification. The novelty of this method lies in applying rough set theory for extracting classification rules and computing fuzzy membership values directly from decision table after attributes reduction on a real-valued attribute table consisting of classification features. The successful application of this approach in hyperspectral remote sensing images mineral classification illustrates the flexibility and practicality of this new approach.

Citation Download Citation

Zhaocong Wu and Deren Li "A rough sets approach of hyperspectral image classification", Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 604310 (3 November 2005); https://doi.org/10.1117/12.654876

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