USA As Represented by Secretary of the Navy

Daniel G Haas

Jeffrey H Bowles

Peter J Palmadesso

John A Antoniades

Mark M Baumback

John M Grossman

John Fisher

John M Grossmann

A method for defining targets in hyperspectral image data of a scene, has the steps: (1) identifying a target within the scene; (2) fitting a plurality of conjoined hyperplanes about the target in the scene; and (3) fitting a shape to the identified target by shrinkwrapping the hyperplanes about the target by translating and rotating the hyperplanes until a specified fit parameter is satisfied.

Hyperspectral visualization extensible workbench

The Intelligent Hypersensor Processing System (IHPS) is a system for the rapid detection of small, weak, or hidden objects, substances, or patterns embedded in complex backgrounds, providing fast adaptive processing for demixing and recognizing patterns or signatures in data provided by certain types of &ldquo;hypersensors&rdquo;. SERENE introduces an improved noise reduction algorithm useful on systems which use hypersensors such as IHPS, or other systems which use sensors which are hyper or multi spectral. The SERENE technique may be employed through out the Learning Module Processor pipelining and further processes the data stream to filter intrinsic and extrinsic noise, and minimize exemplar noise effects in the exemplar set. This system represents an alternative to prior systems for hidden object detection by solving the problems encountered when attempting to detect hidden objects/targets in dynamic scenarios at real-time.

System for eliminating or reducing exemplar effects in multispectral or hyperspectral sensors

The Compression of Hyperdata with ORASIS Multisegment Pattern Sets, (CHOMPS), system is a collection of algorithms designed to optimize the efficiency of multi spectral data processing systems. The CHOMPS system employs two types of algorithms, Focus searching algorithms and Compression Packaging algorithms. The Focus algorithms employed by CHOMPS reduce the computational burden of the prescreening process by reducing the number of comparisons necessary to determine whether or not data is redundant, by selecting only those exemplars which are likely to result in the exclusion of the incoming sensor data for the prescreener comparisons. The Compression Packaging algorithms employed by CHOMPS, compress the volume of the data necessary to describe what the sensor samples. In the preferred embodiment these algorithms employ the Prescreener, the Demixer Pipeline and the Adaptive Learning Module Pipeline to construct a compressed data set. The compression is realized by constructing the data set from the exemplars defined in the prescreening operation and expressing those exemplars in wavespace with the necessary scene mapping data, or further processing the exemplars through the adaptive learning pipeline 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.

Compression of hyperdata with ORASIS multisegment pattern sets (CHOMPS)

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Lothian, MD, United States of America

Daniel G Haas