AUTOMATED SCENE UNDERSTANDING:

Automated scene understanding is the task of being able to automatically describe a scene (algorithmically) given input data and imagery. Many scene understanding approaches in the machine learning literature rely on semantic segmentation approaches which require precise, detailed label information for a large body of training imagery. Yet this data is often difficult, if not infeasible, to obtain. Furthermore, most semantic segmentation approaches can only learn crisp segmentations of a scene and are incapable of describing regions of transition or gradients. FINS has ongoing studies for developing and implementing semi-supervised learning approaches that can use all available information about a scene and mitigate the need for large, precise training sets. Geo-tagged social media data, map data, analyst key-points, and any available ground-truth or scene information are used to guide this analysis. This type of ancillary data provides scene information that can be leveraged during hyperspectral analysis. However, this data is likely to be noisy, incomplete, or inaccurately registered. FINS is developing a framework and algorithms for semi-supervised multi- sensor fusion that can learn from imprecise labels. FINS is also developing soft segmentation approaches that can identify and describe gradients and regions of transitions in input imagery. This will allow for automated understanding of a wider range and more realistic scenarios than previously possible.

TARGET CHARACTERIZATION FROM UNCERTAIN LABELS:

Most supervised Machine Learning algorithms assume that each training data point is paired with an accurate training label. However, obtaining accurate training label information is often times consuming and expensive. Human annotators may be inconsistent when labeling a data set, providing inherently imprecise label information. Training an accurate classifier or learning a representative target signature from this sort of uncertainly labeled training data is extremely difficult in practice. FINS is developing approaches that can perform target characterization even with this uncertainty. Target characterization learns characteristic, salient features for targets of interest which can then be used in detection applications. Research in this area provides practical methods that allow application of machine learning methods to problems that were previously bottle-necked by ground-truth requirements needed by traditional machine learning approaches.