Integration of geographic information system and RADARSAT synthetic aperture radar data using a self-organizing map network as compensation for realtime ground data in automatic image classification

Shepherd, Ifan D. H. and Passmore, Peter J. and Kamal, Mohammad M. (2010) Integration of geographic information system and RADARSAT synthetic aperture radar data using a self-organizing map network as compensation for realtime ground data in automatic image classification. Journal of Applied Remote Sensing, 4 (043534). pp. 1-13. ISSN 1931-3195

[img]
Preview
PDF
Download (1MB)

Abstract

The paper presents results of using advanced techniques such as Self-Organizing feature Map (SOM) to incorporate a GIS data layer to compensate for the limited amount of
real-time ground-truth data available for land-use and land-cover mapping in wet-season conditions in Bangladesh based on multi-temporal RADARSAT-1 SAR images. The experimental results were compared with those of traditional statistical classifiers such as Maximum Likelihood, Mahalanobis Distance, and Minimum Distance, which are not suitable for incorporating low-level GIS data in the image classification process. The performances of the classifiers were evaluated in terms of the classification accuracy with respect to the collected real-time ground truth data. The SOM neural network provided the highest overall accuracy when a GIS layer of land type classification with respect to the depth and duration of regular flooding was used in the network. Using this method, the overall accuracy was around 15% higher than the previously mentioned traditional classifiers at 79.6% where the training data covered only 0.53% of the total image. It also achieved higher accuracies for more classes in comparison to the other classifiers.

Item Type: Article
Research Areas: A. > Business School
A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 6578
Notes on copyright: With thanks to publisher for allowing official PDF versions of published work.
Useful Links:
Depositing User: Prof Ifan Shepherd
Date Deposited: 05 Oct 2010 09:18
Last Modified: 16 Dec 2015 17:33
URI: http://eprints.mdx.ac.uk/id/eprint/6578

Actions (login required)

Edit Item Edit Item

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year