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 Mostafa (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 (1) , 043534. pp. 1-13. ISSN 1931-3195 [Article] (doi:10.1117/1.3457166)

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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.

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