This award will provide for the development of a comprehensive system that can efficiently and intelligently extract, analyze and manage very large hyperspectral datasets used for classifying a large variety of land covers in environmentally sensitive ecosystems.
Hyperspectral data provide unprecendented spectral resolution which can translate to far superior characterization of remotely sensed areas, but pose significant challenges because of the large data volumes, high dimensionality, little labelled data and large number of potential land cover types or classes. These challenges are being addressed by new adaptive feature space reduction methods that exploit spectral correlations, by semi-supervised and active learning methods for dealing with small training sets, and by knowledge reuse and transfer mechanisms that adapt models developed for one area to new regions with related characteristics. In parallel, a knowledge repository that helps rapidly identify the most pertinent features/classes for a given area, will be built to substantially reduce data storage requirements and processing time.
This inter-disciplinary project requires tight interaction between data acquisition and processing/analysis, and will provide insights for other engineering problems as well. The visual nature of results from analysis of remotely sensed data make it a powerful modality of introducing the general population to issues of broad concern, such as the impact of global warming and disaster management. Finally, the knowledge transfer mechanisms will be useful for rapidly adapting existing solutions to somewhat different but related problems, thus substantially increasing the utility of existing point solutions in several application domains.
