Abstract: A method for anomaly resistant detection and identification of an object signature in hypersensor data includes processing hypersensor data using a multi-dimensional matched filter to distinguish spectra that produce anomaly-generated false alarms from target spectrum, and suppressing the spectra that produce anomaly-generated false alarms.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 “hypersensors”. 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.

Abstract: 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.

Abstract: 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 "hypersensors". 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.IHPS accomplishes this by forming, a series of pattern vectors through the concatenation of the outputs of multiple sensors. Each sensor measures a different attribute of the system being observed, and has a consistent relationship to all the other sensors. The data stream form the sensors is entered into a processing system which employs a parallel-pipeline architecture. The data stream is simultaneously sent to two separate processor pipes.