Vegetation classification using hyperspectral and multi-angular remote sensing data

In this study, vegetation cover type classification was investigated using CHRIS data over agricultural scenes acquired across the 2004 growing season. Spectral indices sensitive to crop chlorophyll content and leaf area index were first calculated from CHRIS nadir data in May, June and July. The seasonality of these indices was analyzed and employed to identify crop types in the study area. To further improve the classification accuracy, the angular signatures of the vegetation canopies were derived from the nadir and off- nadir data in the red and near-infrared band using the kernel-driven Ross_Thick and Li-Sparse model. The coefficients of the kernel-based model were then used for crop type classification, together with the spectral indices derived from nadir data. Preliminary results show that the additional angular information can slightly improve the classification accuracy.