SIT-FUSE - Segmentation, Instance Tracking, and data Fusion Using multi-SEnsor imagery(NPO-52538-1)

Previously, we developed an unsupervised machine learning method that uses level 1 (L1) radiances from various satellite and airborne imagers, as well as the fusion of datasets, where applicable, as input. This method was tested on data collected during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign,as well as other data fusion and segmentation datasets. The clustered output of the unsupervised models accurately identifies objects wild fires, smoke plumes, and burn scars, and establishes the basis for future automated tracking capabilities.