Hypersensor Data Compression & Pattern Recognition Software

The CHOMPS/ORASIS system is a collection of algorithms designed to optimize the efficiency of multispectral and hyperspectral data processing systems.


20:1 compression with virtually no loss. Perform analysis on compressed data, without reconstructing the entire image set.

ORASIS (Optical Real Time Spectral Identification System) is a method for finding endmembers in a hypersensor image by selecting representative exemplars in a prescreening process, in nearly real time, without assuming any a priori knowledge of the scene. 

CHOMPS is then used to optimize the processing efficiency by employing two types of algorithms, Focus Searching algorithms and Compression Packaging algorithms. The Focus Searching algorithms reduce the computational burden by reducing the number of comparisons necessary to determine whether or not data is redundant. Compression is realized by constructing the data set from the exemplars defined in the prescreening operation and expressing those exemplars in wave space with the necessary scene mapping data, or further processing the exemplars and expressing the exemplars in terms of endmembers, to facilitate the efficient storage, download and the later reconstruction of the complete data set with minimal deterioration of signal information.

BENEFITS:

• Efficient - compress hypersensor data up to 20:1 with virtually no loss
• Easy - perform analysis on compressed data, without reconstructing the entire image set
• Effective – find desired information in a complex matrix of information
• Economical - can operate on a Pentium class PC without the need for expensive workstation