Advances in Supervised and Semi-Supervised Machine Learning for Image Analysis of Multi-Modal Geospatial Imagery Data

Thursday, October 25, 2018 - 2:00pm - 2:50pm
Keller 3-180
Saurabh Prasad (University of Houston)
Recent advances in optical sensing technology (miniaturization and low-cost architectures for spectral imaging) and sensing platforms from which such imagers can be deployed (e.g. handheld devices, unmanned aerial vehicles) have the potential to enable ubiquitous multispectral and hyperspectral imaging on demand to support geospatial analysis. Often, however, robust analysis with such data is challenging due to limited/noisy ground-truth, and variability due to illumination, scale and atmospheric conditions. In this talk, I will review recent advances in the areas of subspace learning, structured sparsity and Bayesian inference towards robust single and multi-sensor geospatial imaging while providing robustness to the aforementioned challenges. I will also present how some of these ideas bear synergy with emerging trends in deep learning and can support robust deep learning under the small-sample-size scenario. I will discuss the algorithmic developments and present results with a benchmark urban ground-cover classification task, as well as an ecosystem monitoring multi-sensor geospatial dataset.