Research project SR/67/146 (Research action SR)
In ecosystems where two or more vegetation types or plant species co-exist, the success of remote sensing (RS) based applications is still limited. This is because image interpretation is complicated by the high spectral similarity between the different plant species which is strengthened by the composite nature of the reflectance signals obtained by the RS sensor.
The main objective of this project is to explore the potential of hyperspectral image analysis for individual plant species mapping. The potential of Spectral Mixture Analysis (SMA) and multi-temporal image classification is evaluated. It is anticipated that by assimilating both techniques the spectral similarity problem in intimately mixed vegetation systems can be addressed effectively. A multi-temporal unmixing based feature extraction technique is therefore proposed. The basic hypothesis is that the spectral similarity and spectral mixture problem can effectively be mitigated by integration of iterative mixture analysis cycles and automated feature selection in a hyperdimensional spectro-temporal feature space.
