Comparison between object- and pixel-level approaches for change detection in multispectral images by using neural networks

Hassiba Nemmour; Youcef Chibani

doi:10.1117/12.509934

5 February 2004 Comparison between object- and pixel-level approaches for change detection in multispectral images by using neural networks

Hassiba Nemmour, Youcef Chibani

Author Affiliations +

Proceedings Volume 5238, Image and Signal Processing for Remote Sensing IX; (2004) https://doi.org/10.1117/12.509934
Event: Remote Sensing, 2003, Barcelona, Spain

Abstract

We propose in this paper the investigation of the change detection approaches based on the pixel level and the object level. The pixel level approach is based on the simultaneous analysis of multitemporal data, while the object level approach uses a comparative analysis of independently produced classifications of data. Thereby, the comparison is established by using the multilayer neural network classifier. Usually, the backpropagation algorithm is used as a training rule. In this paper, we investigate the use of the Kalman filtering (KF) as the training algorithm for detecting changes in remotely sensed imagery. By using SPOT images and evaluation criteria, the detailed comparison indicates that the KF algorithm is preferable compared to the BP algorithm in terms of convergence rate, stability and change detection accuracy.

Citation Download Citation

Hassiba Nemmour and Youcef Chibani "Comparison between object- and pixel-level approaches for change detection in multispectral images by using neural networks", Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); https://doi.org/10.1117/12.509934

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