1 June 2010 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
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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.
Mohammad Mostafa Kamal, Mohammad Mostafa Kamal, Peter J. Passmore, Peter J. Passmore, Ifan D. H. Shepherd, Ifan D. H. Shepherd, } "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," Journal of Applied Remote Sensing 4(1), 043534 (1 June 2010). https://doi.org/10.1117/1.3457166 . Submission:
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