Abstract: Methods for reducing dimensionality of hyperspectral image data having a number of spatial pixels, each associated with a number of spectral dimensions, include receiving sets of coefficients associated with each pixel of the hyperspectral image data, a set of basis vectors utilized to generate the sets of coefficients, and either a maximum error value or a maximum data size. The methods also include calculating, using a processor, a first set of errors for each pixel associated with the set of basis vectors, and one or more additional sets of errors for each pixel associated with one or more subsets of the set of basis vectors. Utilizing such errors calculations, an optimum size of the set of basis vectors may be ascertained, allowing for either a minimum amount of error within the maximum data size, or a minimum data size within the maximum error value.

Abstract: In accordance with various aspects of the disclosure, a detecting engine for detecting targets/materials in hyperspectral scenes is disclosed. The detecting engine combines data partitioning and dimensionality reduction to reduce the number of computations needed to identify in which pixels in a hyperspectral scene a given material is present. Computation reduction (in some instances, by two fold) greatly impacts the speed of and power consumed by the detecting engine making the engine suitable for hyperspectral imaging of large scenes, processing using many filters per pixel, or missions requiring testing large numbers of reference spectra to see which are present in a scene.

Abstract: In accordance with various aspects of the disclosure, a method, system, and computer readable media having instructions for processing images is disclosed. For example, the method includes determining a suspicious pixel suspected of causing an artifact in a measurement as a function of a statistical analysis of a collection of samples representing residual error values associated with a subject focal plane pixel measuring one waveband at different times. Based on the determination of the suspicious pixel, a pattern of residual error values is identified that is indicative of the artifact caused by the suspicious pixel. A correcting time-dependent offset determined that is substantially reciprocal to the identified pattern of residual error values. The correcting time-dependent offset is applied to the measurement to correct for artifact in the measurement.

Abstract: A method for reducing dimensionality of hyperspectral images includes receiving a hyperspectral image having a plurality of pixels. The method may further include establishing an orthonormal basis vector set comprising a plurality of mutually orthogonal normalized members. Each of the mutually orthogonal normalized members may be associated with one of the plurality of pixels of the hyperspectral image. The method may further include decomposing the hyperspectral image into a reduced dimensionality image, utilizing calculations performed while establishing said orthonormal basis vector set. A system configured to perform the method may also be provided.

Abstract: In accordance with various aspects of the disclosure, a method, system, and computer readable media having instructions for processing images is disclosed. For example, the method includes determining a suspicious pixel suspected of causing an artifact in a measurement as a function of a statistical analysis of a collection of samples representing residual error values associated with a subject focal plane pixel measuring one waveband at different times. Based on the determination of the suspicious pixel, a pattern of residual error values is identified that is indicative of the artifact caused by the suspicious pixel. A correcting time-dependent offset determined that is substantially reciprocal to the identified pattern of residual error values. The correcting time-dependent offset is applied to the measurement to correct for artifact in the measurement.

Abstract: Methods for reducing dimensionality of hyperspectral image data having a number of spatial pixels, each associated with a number of spectral dimensions, include receiving sets of coefficients associated with each pixel of the hyperspectral image data, a set of basis vectors utilized to generate the sets of coefficients, and either a maximum error value or a maximum data size. The methods also include calculating, using a processor, a first set of errors for each pixel associated with the set of basis vectors, and one or more additional sets of errors for each pixel associated with one or more subsets of the set of basis vectors. Utilizing such errors calculations, an optimum size of the set of basis vectors may be ascertained, allowing for either a minimum amount of error within the maximum data size, or a minimum data size within the maximum error value.

Abstract: Computer implemented methods for compressing 3D hyperspectral image data having a plurality of spatial pixels associated with a hyperspectral image, and a number of spectral dimensions associated with each spatial pixel, include receiving, using a processor, the 3D hyperspectral image data, a set of basis vectors associated therewith, and either a maximum error amount or a maximum data size. The methods also include partitioning the 3D hyperspectral image data into a plurality of 2D images, each associated with one of the number of spectral dimensions, and an associated one of the set of basis vectors. The methods additionally include ranking the set of basis vectors if not already ranked. The methods may further include iteratively applying lossy compression to the 2D images, in an order determined by the ranking. Other embodiments and features are also disclosed.

Abstract: A method for reducing dimensionality of hyperspectral images includes receiving a hyperspectral image having a plurality of pixels. The method may further include establishing an orthonormal basis vector set comprising a plurality of mutually orthogonal normalized members. Each of the mutually orthogonal normalized members may be associated with one of the plurality of pixels of the hyperspectral image. The method may further include decomposing the hyperspectral image into a reduced dimensionality image, utilizing calculations performed while establishing said orthonormal basis vector set. A system configured to perform the method may also be provided.

Abstract: A method of filtering hyperspectral image data associated with a hyperspectral image to produce a detection image data having a plurality of pixels, where the detection image data is associated with the degree to which a target may be present in a pixel. The method also includes adaptively processing the detection image data to determine a background variation in the plurality of pixels. The method additionally includes establishing a plurality of spatial filters for the detection image data, where each of the plurality of spatial filters are associated with energy being received at different locations on each of the plurality of pixels, and where the outputs of the plurality of spatial filters are weighted by the variation in background. The method further includes applying each of the plurality of spatial filers to the detection image data, such that each of the plurality of pixels are associated with a selected one of the plurality of spatial filters.

Abstract: A method of clustering and reducing hyperspectral image data having a plurality of spatial pixels, and a plurality of spectral dimensions associated with each spatial pixel, includes computing an initial basis vector associated with the hyperspectral image data, unmixing the initial basis vector with the hyperspectral image data to generate an initial set of coefficients and an associated set of residual vectors, generating a set of clusters based on the initial set of coefficients, and iteratively computing one or more additional basis vectors and updating the set of clusters. The iterative computing includes calculating a subsequent basis vector based on a residual vector associated with a prior unmixing, unmixing the subsequent basis vector with a prior set of residual vectors to generate additional coefficients associated with each pixel, and iteratively computing cluster centers and content including an additional dimension associated with the subsequent basis vector.

Abstract: Computer implemented methods for compressing 3D hyperspectral image data having a plurality of spatial pixels associated with a hyperspectral image, and a number of spectral dimensions associated with each spatial pixel, include receiving, using a processor, the 3D hyperspectral image data, a set of basis vectors associated therewith, and either a maximum error amount or a maximum data size. The methods also include partitioning the 3D hyperspectral image data into a plurality of 2D images, each associated with one of the number of spectral dimensions, and an associated one of the set of basis vectors. The methods additionally include ranking the set of basis vectors if not already ranked. The methods may further include iteratively applying lossy compression to the 2D images, in an order determined by the ranking. Other embodiments and features are also disclosed.

Abstract: A method of clustering and reducing hyperspectral image data having a plurality of spatial pixels, and a plurality of spectral dimensions associated with each spatial pixel, includes computing an initial basis vector associated with the hyperspectral image data, unmixing the initial basis vector with the hyperspectral image data to generate an initial set of coefficients and an associated set of residual vectors, generating a set of clusters based on the initial set of coefficients, and iteratively computing one or more additional basis vectors and updating the set of clusters. The iterative computing includes calculating a subsequent basis vector based on a residual vector associated with a prior unmixing, unmixing the subsequent basis vector with a prior set of residual vectors to generate additional coefficients associated with each pixel, and iteratively computing cluster centers and content including an additional dimension associated with the subsequent basis vector.

Abstract: In accordance with various aspects of the disclosure, a system, a method, and computer readable medium having instructions for processing images is disclosed. For example, the method includes receiving an input datacube from which an input image is derived. The input datacube is transformed into a residual datacube by projecting out basis vectors from each spatial pixel in the input datacube, the residual datacube being used to derive a residual image. A statistical parameter value for samples of each focal plane pixel in the residual image is determined. Anomalous focal plane pixels are identified based upon a comparison of the determined statistical parameter value with the respective determined statistical parameter values of remaining focal plane pixels. Another comparison of residual values for each scanned sample of the identified anomalous focal plane pixels with values of corresponding scanned samples in the input datacube is performed.

Abstract: Provided is a process and system for detection of sparse or otherwise weak targets in a hyperspectral image. The method includes receiving a hyperspectral image having a plurality of pixels, with each pixel having a respective spectrum. Multiple mean spectra are selectively determined for respective sub-regions of the hyperspectral image. The subset mean spectra are selectively removed from respective pixels, thereby improving image fidelity due to sensor artifacts. Additionally, target detection of such an adjusted image can be determined by one or more of matched filter techniques or by partial un-mixing. In some embodiments target detection is enhanced by combining a measure of target match with a measure of un-match. Target detection can be further improved by application of rules, for example, related to target detection threshold.

Abstract: Methods for reducing dimensionality of hyperspectral image data having a number of spatial pixels, each associated with a number of spectral dimensions, include receiving sets of coefficients associated with each pixel of the hyperspectral image data, a set of basis vectors utilized to generate the sets of coefficients, and either a maximum error value or a maximum data size. The methods also include calculating, using a processor, a first set of errors for each pixel associated with the set of basis vectors, and one or more additional sets of errors for each pixel associated with one or more subsets of the set of basis vectors. Utilizing such errors calculations, an optimum size of the set of basis vectors may be ascertained, allowing for either a minimum amount of error within the maximum data size, or a minimum data size within the maximum error value.

Abstract: Computer implemented methods for compressing 3D hyperspectral image data having a plurality of spatial pixels associated with a hyperspectral image, and a number of spectral dimensions associated with each spatial pixel, include receiving, using a processor, the 3D hyperspectral image data, a set of basis vectors associated therewith, and either a maximum error amount or a maximum data size. The methods also include partitioning the 3D hyperspectral image data into a plurality of 2D images, each associated with one of the number of spectral dimensions, and an associated one of the set of basis vectors. The methods additionally include ranking the set of basis vectors if not already ranked. The methods may further include iteratively applying lossy compression to the 2D images, in an order determined by the ranking. Other embodiments and features are also disclosed.

Abstract: In accordance with various aspects of the disclosure, a system, a method, and computer readable medium having instructions for processing images is disclosed. For example, the method includes receiving, at an image processor, a set of images corresponding to a scene changing with time, decomposing, at the image processor, the set of images to detect static objects, leaner objects, and mover objects in the scene, the mover objects being objects that change spatial orientation in the scene with time, and compressing, using the image processor, the mover objects in the scene separately at a rate different from that of the static objects and the leaner objects for storage and/or transmission.

Abstract: Methods for compressing hyperspectral image data include receiving sets of coefficients associated with each pixel of the hyperspectral image data, a set of basis vectors utilized to generate the dimensionally reduced data from the hyperspectral image, and either a maximum error value or maximum data size. The methods include associating the coefficients with a subset of the basis vectors, and storing the association. Methods of decompressing the compressed hyperspectral image data are also disclosed, utilizing the association.

Abstract: Methods for compressing hyperspectral image data include receiving sets of coefficients associated with each pixel of the hyperspectral image data, a set of basis vectors utilized to generate the dimensionally reduced data from the hyperspectral image, and either a maximum error value or maximum data size. The methods include associating the coefficients with a subset of the basis vectors, and storing the association. Methods of decompressing the compressed hyperspectral image data are also disclosed, utilizing the association.

Abstract: Methods for reducing dimensionality of hyperspectral image data having a number of spatial pixels, each associated with a number of spectral dimensions, include receiving sets of coefficients associated with each pixel of the hyperspectral image data, a set of basis vectors utilized to generate the sets of coefficients, and either a maximum error value or a maximum data size. The methods also include calculating, using a processor, a first set of errors for each pixel associated with the set of basis vectors, and one or more additional sets of errors for each pixel associated with one or more subsets of the set of basis vectors. Utilizing such errors calculations, an optimum size of the set of basis vectors may be ascertained, allowing for either a minimum amount of error within the maximum data size, or a minimum data size within the maximum error value.