Algorithmic Comparison of Supervised Classification Method Based on ALOS AVNIR-2 imagery for Mangrove Mapping
The goal of this research was to provide an appropriate algorithm for mapping the mangrove area in Anday. Supervised classification method applied that consisted of several algorithmic alternatives such as parallel piped, minimum distance algorithm, mahalanobis distance, maximum likelihood, and spectral angle mapper. In addition, this study was conducted by applying the image process of a near-infrared band of ALOS AVNIR-2 and then analysis was carried out to leverage the accuracy of the range of spectral values through field survey and to test the accuracy of mangrove zonation maps which was based the supervised classification method. The results revealed that the overall accuracy of parallelepiped was 29%, and 41.18% for minimum distance algorithm, mahalanobis distance was 58.82%, the maximum likelihood was 50%, and spectral angle mapper was 58.82%.
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