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., Passmore, Peter J. ORCID: https://orcid.org/0000-0002-5738-6800 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 (doi:10.1117/1.3457166)

[img]
Preview
PDF
Download (1MB) | Preview

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: Ifan Shepherd
Date Deposited: 05 Oct 2010 09:18
Last Modified: 12 Oct 2019 12:49
URI: https://eprints.mdx.ac.uk/id/eprint/6578

Actions (login required)

Edit Item Edit Item