Critical to improving these capabilities is improving remote sensor processing. Object of interest detection of target images via remote sensors requires the ability to extract distinguishable objects from a noisy environment: one of the most challenging tasks in remote sensor processing.  The data acquired from different remote sensors have different resolution levels and reflect different physical and geometrical properties of the objects represented by the images.  Any automatic system for image analysis therefore cannot rely on intuitive definitions of objects, but should instead be based on the computable property of the object. 

Leveraging KBSI's extensive experience in sensory data processing, the KBSI team devising and developing an innovative Multi-Spectral Feature Fusion (MSFF) framework for the automatic fusion of mission-critical data from hyper spectral data sources. Data acquired from these sources is in detecting evolving threat scenarios. The proposed framework will improve target detection from data that has been obtained from sensors with a wide diapason spectrum, resulting in more effective multi-sensor processing and object of interest detection, association, and identification.

This Phase I MSFF project developing the concept of operation and algorithms. While this technology is particularly suited for the U.S. Navy and Air Force, the Department of Defense, and the Missile Defense Agency, and number of commercial applications can also benefit. Healthcare systems can use MSFF technology to improve the diagnostics of diseased tissue states. MSFF can also be used by environmental services to support alternative methods for natural resources exploration and reserves estimation.