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.
Document Type: Research Article
Publication date: 01 August 2014
- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies. - Editorial Board
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