The Machine Learning and Signal Processing Lab
at the University of Houston

Multi-Scale, Multi-Sensor GeoAI
Multi-Sensor, Multi-Scale Geospatial Image Analysis is a key application focus of our lab. Modern geospatial imaging via both active and passive sensing modalities (e.g. Multispectral, Hyperspectral, LiDAR and SAR) aboard platforms such as satellites as well as manned and unmanned aircraft are a double-edge-sword. On the one hand they can facilitate a remarkable understanding of the earth. On the other hand, the high dimensionality of modern multi-channel imagers, along with the limited ground truth available for remote sensing tasks make effective image analysis fraught with challenges that must be addressed. See the following page for details on our ongoing work on GeoAI. NASA GeoAI Github

AI in Biomedicine
Modern biomedical imaging systems (e.g. FTIR Microscopy and highly-multiplexed immuno-flourescence microscopy) can capture very high resolution imagery and provide rich multi-channel characterization of the underlying biological processes. As an example, by leveraging a multitude of biomarkers when acquiring IF images, highly multiplexed IF microscopy has the potential to provide a rich multi-channel representation of the tissue for accurate identification of all relevant cell phenotypes, which constitute the tissue and exhibit specific cell patterning distributions that define unique anatomical regions that make up an organ structure (e.g. within the rat brain). To effectively utilize emerging machine learning paradigm on these data, one needs to address the unique nuances and challenges posed by such imagery.