Hyperspectral Data Dimensionality Reduction and the Impact of Multi-seasonal Hyperion EO-1 Imagery on Classification Accuracies of Tropical Forest Species

Synchronizing hyperspectral data acquisition with phenological changes in a tropical forest can generate comprehensive information for their effective management. The present study was performed to identify a suitable dimensionality reduction method for better classification and to evaluate the impact of seasonality on classification accuracy of tropical forest cover. EO-1 Hyperion images were acquired for three different seasons (summer (April), monsoon (October), and winter (January)). Spectral signatures of pure patches of Teak, Bamboo, and mixed species covers are significantly different across the three seasons indicating distinctive phenology of each cover. Kernel Principal Component Analysis (k-PCA) is more suitable for dimensionality reduction for these covers. The three vegetation covers classified using images of three seasons achieved the best classification accuracies using k-PCA with maximum likelihood classifier for the monsoon season with overall accuracies of 83 to 100 percent for single species, 74 to 81 percent for two species, and 72 percent for three species respectively.