Super-resolution mapping using multiple observations and Hopfield neural network

Anuar M. Muad; Giles M. Foody

doi:10.1117/12.865092

13 October 2010 Super-resolution mapping using multiple observations and Hopfield neural network

Anuar M. Muad, Giles M. Foody

Proceedings Volume 7830, Image and Signal Processing for Remote Sensing XVI; 783003 (2010) https://doi.org/10.1117/12.865092
Event: SPIE Remote Sensing, 2010, Toulouse, France

Abstract

Super-resolution mapping is used to produces thematic maps at a scale finer than the source images. This paper presents a new super-resolution mapping approach that exploits the typically fine temporal resolution of coarse spatial resolution images as it input and an adoption of an active threshold surface using Hopfield neural network as a means to map land cover at a sub-pixel scale. The results demonstrated that the proposed technique is slightly more accurate than the existence technique in terms of site specific accuracy and produce better visualization on individual land cover map.

Citation Download Citation

Anuar M. Muad and Giles M. Foody "Super-resolution mapping using multiple observations and Hopfield neural network", Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 783003 (13 October 2010); https://doi.org/10.1117/12.865092

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