Abstract I argue that the spectra in a hyperspectral datacube will usually lie in several low dimensional subspaces, and that these subspaces are more easily estimated from the data than the endmembers. I present an algorithm for finding the subspaces. The algorithm fits the data with a Gaussian mixture model, in which the means andcovariance […]
Clay Spence
SRI Author
Analyzing hyperspectral images into multiple subspaces using Gaussian mixture models
I argue that the spectra in a hyperspectral datacube will usually lie in several low-dimensional subspaces, and that these subspaces are more easily estimated from the data than the endmembers. I present an algorithm for finding the subspaces. The algorithm fits the data with a Gaussian mixture model, in which the means and covariance matrices are parameterized in terms of the subspaces. The locations of materials can be inferred from the fit of library spectra to the subspaces. The algorithm can be modified to perform material detection. This has better performance than standard algorithms such as ACE, and runs in real time.
Pattern of life analysis for diverse data types
SRI has developed a system to automatically analyze the Pattern of Life (PoL) of ports, routes and vessels from a large collection of AIS data. The PoL of these entities are characterized by a set of intuitive and easy to query semantic attributes. The prototype system provides an interface to ingest other types of information such as WAAS (Wide Area Aerial Surveillance) and GDELT (Global Database of Events, Language, and Tone) to augment knowledge of the Area of Operations. It can interact with users by answering questions and simulating what-if scenarios to keep human in the processing loop.
