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Journal of Applied Remote Sensing

Integration of geographic information system and RADARSAT synthetic aperture radar data using a self-organizing map network as compensation for real-time ground data in automatic image classification
Author(s): Mohammad Mostafa Kamal; Peter J. Passmore; Ifan D. H. Shepherd
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Paper 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.

Paper Details

Date Published: 1 June 2010
PDF: 14 pages
J. Appl. Rem. Sens. 4(1) 043534 doi: 10.1117/1.3457166
Published in: Journal of Applied Remote Sensing Volume 4, Issue 1
Show Author Affiliations
Mohammad Mostafa Kamal, Univ. of Saskatchewan (Canada)
Peter J. Passmore, Middlesex Univ. (United Kingdom)
Ifan D. H. Shepherd, Middlesex Univ. (United Kingdom)

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