Remote sensing image change detection using Gray system theory

Yufeng Gui; Xinping Xiao; Jixian Zhang; Zongjian Lin; Yuanying Mou

doi:10.1117/12.441374

25 September 2001 Remote sensing image change detection using Gray system theory

Yufeng Gui, Xinping Xiao, Jixian Zhang, Zongjian Lin, Yuanying Mou

Proceedings Volume 4548, Multispectral and Hyperspectral Image Acquisition and Processing; (2001) https://doi.org/10.1117/12.441374
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

Abstract

Remote sensing image change detection techniques are widely used in environmental change detection such as landuse change monitor, flood monitor. Many change detection techniques are used in practice today. This paper reports the development of techniques based on artificial neural networks and presents a new method of integrating artificial neural networks (ANN) with gray system theory for remote sensing image change detection. Gray system theory, founded by Professor Deng Julong, can handle undetermined problem .It is effective when the sample datum can not satisfy some distribution. The accuracy of image change detection based on traditional ANN is influenced by some factors such as network architecture, training set. The number of hidden layers and the number of nodes in a hidden layer are not easy to deduce. The traditional neural network architecture which gives the best results for image change detection can only be determined experimentally, and this can be a lengthy process especially for large image. This paper presents a new method that the number of nodes in hidden layers is deduced by using gray correlation analysis in gray system theory. A neural network based change detection system using the backpropagation training is developed. The trained three-layered neural network was able to provide information of changes and detect land-cover change with an overall accuracy of 91.3 percent. Using the same training data, a maximum-likelihood supervised classification produced an accuracy of 85.1 percent. The experimental results by using multitemporal TM imagery and SPOT imagery. Findings of this study demonstrated the potential and advantages of using neural network and gray system theory in multitemporal change analysis.

Citation Download Citation

Yufeng Gui, Xinping Xiao, Jixian Zhang, Zongjian Lin, and Yuanying Mou "Remote sensing image change detection using Gray system theory", Proc. SPIE 4548, Multispectral and Hyperspectral Image Acquisition and Processing, (25 September 2001); https://doi.org/10.1117/12.441374

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
4 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Neural networks

Remote sensing

Artificial neural networks

Image processing

Image classification

Network architectures

Associative arrays

Show All Keywords

Keywords/Phrases

Search In:

Publication Years