Combining variable precision rough set and neural network in remote sensing image classification

Qiong Wang; Jian Liu

doi:10.1117/12.831345

30 October 2009 Combining variable precision rough set and neural network in remote sensing image classification

Qiong Wang, Jian Liu

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74960P (2009) https://doi.org/10.1117/12.831345
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

This paper presents a new approach of Remotely Sensed data classification based on Variable Precision Rough set(VPRS) and BP neural network, compared to traditional rough sets, VPRS is more robust to noise and can generate more concise and representative classification rules of the remote sensing image. After the rules are deduced, they are fed to the BP neural network, which results in short training time and a high classification accuracy.

Citation Download Citation

Qiong Wang and Jian Liu "Combining variable precision rough set and neural network in remote sensing image classification", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960P (30 October 2009); https://doi.org/10.1117/12.831345

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