Abstract
Several classification algorithms for pattern recognition had been tested in the mapping of tropical forest cover using airborne hyperspectral data. Results from the use of Maximum Likelihood (ML), Spectral Angle Mapper (SAM), Artificial Neural Network (ANN) and Decision Tree (DT) classifiers were compared and evaluated. It was found that ML performed the best followed by ANN, DT and SAM with accuracies of 86%, 84%, 51% and 49% respectively.
Highlights
The increasing application of remote sensing for forest monitoring and inventory is seen as a cost effective source of information for the practice of sustainable forest management
Some of the applications have proven to be successful in the inventory of temperate forests[1], doubts have been raised concerning the ability of the sensor to effectively discriminate among the rich diversity of flora of tropical forests
Several work devoted to the studies on the classification of hyperspectral remote sensing data have been reported in the literature, for example [2,3]
Summary
The increasing application of remote sensing for forest monitoring and inventory is seen as a cost effective source of information for the practice of sustainable forest management. Over the past few decades, the emergence of hyperspectral sensors that enables the acquisition of data with increased number of spectral bands and higher spectral resolution has certainly give significant impacts on our ability to map forest. Some of the applications have proven to be successful in the inventory of temperate forests[1], doubts have been raised concerning the ability of the sensor to effectively discriminate among the rich diversity of flora of tropical forests. This issue can be looked at by examining the effectiveness of several classification algorithms in classifying hyperspectral data of tropical forest. The performance of the classifiers will be assessed for the mapping of Malaysian tropical forest using hyperspectral data
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