Improve the use of land cover data by developing an advanced framework for robust classification using multi-source datasets:
Develop, validate and optimize a generalized multi-kernel, active learning (MKL-AL) pattern recognition framework for multi-source data fusion.
Develop both single- and ensemble-classifier versions (MKL-AL and Ensemble-MKL-AL) of the system.
Utilize multi-source remotely sensed and in situ data to create land-cover classification and perform accuracy assessment with available labeled data; utilize first results to query new samples that, if inducted into the training of the system, will significantly improve classification performance and accuracy.
|Organizations Performing Work
|NASA Headquarters (HQ)
|Washington, District of Columbia
|Goddard Space Flight Center (GSFC)
|Purdue University-Main Campus
|West Lafayette, Indiana