USA As Represented by Secretary of the Navy

Board of Governors for Higher Education, State of Rhode Island and Providence Plantations

Jeffrey H Bowles

Peter J Palmadesso

John A Antoniades

Daniel G Haas

Mark M Baumback

John M Grossman

David A Kidwell

Arne W Fliflet

Richard F Hubbard

Jacob Grun

Charles K Manka

David Lewis

Jay F Sperry

Geoffrey B Smith

John M Grossmann

Michael R Corson

David B Gillis

John Fisher

Melissa K Hornstein

A long-range active thermal imaging system includes an electromagnetic radiation source in the range of from about 10 GHz to about 500 GHz; a beam controller for receiving and retransmitting the electromagnetic radiation in a desired direction toward and onto a surface of a target, thereby heating the target and producing an infrared radiation emission from the target surface; and an infrared imager, e.g. an infrared camera coupled to a processor and display, for receiving the target's infrared radiation emission and generating a thermal image of the target. The radiation source may be selected such that the radiation penetrates into the target to provide a thermal signature, e.g. from subsurface features or objects. The thermal signature exhibits rapid changes that can be monitored in real time. This may allow surface or subsurface details or objects to be detected that would not otherwise be apparent.

Long range active thermal imaging system and method

A spectroscopic detector for identifying the presence of a first substance in the presence of another substance includes a laser for illuminating the substances at a plurality of wavelengths to induce the emission of radiation characteristic of the substance; a spectrometer for measuring the emitted radiation to obtain a plurality of spectral measurement data; and a processor for processing the data. An algorithm combines the data into a composite spectrum and a parameter characteristic of the first substance is identified while information in the composite spectrum contributed by emission of radiation from the other substance is removed to identify the presence of the first substance and obtain a characteristic spectral signature of the first substance. The signature is compared to signatures in a spectral library database, wherein at least some of the library signatures have spectral characteristics differentiated from each other by identifiable spectral characteristics caused by environmental factors.

Method and apparatus for identifying a substance using a spectral library database

A nearer neighbor matching and compression method and apparatus provide matching of data vectors to exemplar vectors. A data vector is compared to exemplar vectors contained within a subset of exemplar vectors, i.e., a set of possible exemplar vectors, to find a match. After a match is found, a probability function assigns a probability value based on the probability that a better matching exemplar vector exists. If the probability that a better match exists is greater than a predetermined probability value, the data vector is compared to an additional exemplar vector. If a match is not found, the data vector is added to the set of exemplar vectors. Data compression may be achieved in a hyperspectral image data vector set by replacing each observed data vector representing a respective spatial pixel by reference to a member of the exemplar set that 'matches' the data vector. As such, each spatial pixel will be assigned to one of the exemplar vectors.

Efficient near neighbor search (ENN-search) method for high dimensional data sets with noise

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)

Growing community of inventors

Burke, VA, United States of America

Jeffrey H Bowles