Abstract: The system and method for super resolution processing at long standoff distances in real-time. The system collects a series of image frames and estimated the shift, rotation, and zoom parameters between each of the image frames. A matrix is generated and then an inversion is applied to the matrix to produce a super resolution image of an area of interest while mitigating the effect of any bad pixels on image quality. In some cases, the area of interest is user-defined and in some cases image chips are provided by tracking software. A fast steering mirror can be used to steer and/or dither the focal plane array.

Abstract: The system and method for super resolution processing at long standoff distances in real-time. The system collects a series of image frames and estimated the sift, rotation, and zoom parameters between each of the image frames. A matrix is generated and then an inversion is applied to the matrix to produce a super resolution image of an area of interest while mitigating the effect of any “bad” pixels on image quality. In some cases, the area of interest in user-defined and in some cases image chips are provided by tracking software. A fast steering mirror can be used to steer and/or dither the focal plane array.

Abstract: A system and method for motion compensation in translational and rotational image smear using a two-axis fast steering mirror and a single-axis gimbal which are controlled simultaneously. A pointing and control module calculates an objective function which can be optimized to find an optimized de-roll rate to compensate for camera boresight rotation in real-time.

Abstract: A rapid anomaly detection approach with corresponding method and system to detect anomalies in scene pixels making up a hyperspectral scene, efficiently, is presented. The approach includes tailoring an approximation of an anomaly score for each scene pixel, individually, based on an “intermediate anomaly score.” The intermediate score is computed using a portion of the terms used to compute the anomaly score. Scene pixels with low intermediate anomaly scores are removed from further processing. The remaining scene pixels are further processed, including computing anomaly scores to detect anomalies in these pixels. Advantageously, examples of the RAND approach process a few terms of all scene pixels, eliminate most scene pixels, and calculate more terms on high anomaly scoring scene pixels as needed.

Abstract: A rapid anomaly detection approach with corresponding method and system to detect anomalies in scene pixels making up a hyperspectral scene, efficiently, is presented. The approach includes tailoring an approximation of an anomaly score for each scene pixel, individually, based on an “intermediate anomaly score.” The intermediate score is computed using a portion of the terms used to compute the anomaly score. Scene pixels with low intermediate anomaly scores are removed from further processing. The remaining scene pixels are further processed, including computing anomaly scores to detect anomalies in these pixels. Advantageously, examples of the RAND approach process a few terms of all scene pixels, eliminate most scene pixels, and calculate more terms on high anomaly scoring scene pixels as needed.

Abstract: The disclosure provides a filtering engine for selecting sparse filter components used to detect a material of interest (or specific target) in a hyperspectral imaging scene and applying the sparse filter to a plurality of pixels in the scene. The filtering engine transforms a spectral reference representing the material of interest to principal components space using the eigenvectors of the scene. It then ranks sparse filter components based on each transformed component of the spectral reference. The filtering engine selects sparse filter components based on their ranks. The filtering engine performs the subset selection quickly because the computations are minimized; it processes only the spectral reference vector and covariance matrix of the scene to do the subset selection rather than process a plurality of pixels in the scene, as is typically done. The spectral filter scores for the plurality of pixels are calculated efficiently using the sparse filter.

Abstract: The disclosure provides a filtering engine for selecting sparse filter components used to detect a material of interest (or specific target) in a hyperspectral imaging scene and applying the sparse filter to a plurality of pixels in the scene. The filtering engine transforms a spectral reference representing the material of interest to principal components space using the eigenvectors of the scene. It then ranks sparse filter components based on each transformed component of the spectral reference. The filtering engine selects sparse filter components based on their ranks. The filtering engine performs the subset selection quickly because the computations are minimized; it processes only the spectral reference vector and covariance matrix of the scene to do the subset selection rather than process a plurality of pixels in the scene, as is typically done. The spectral filter scores for the plurality of pixels are calculated efficiently using the sparse filter.

Abstract: A method for reducing dimensionality of hyperspectral images may include receiving a hyperspectral image having a plurality of pixels. A basis vector set including a number of members may then be established, wherein each of the members comprises a basis vector. For each of the plurality of pixels, a spectral vector for the pixel may be read and decomposed with the members of the basis vector set to derive a residual vector for the pixel. A basis vector for the pixel may then be added to the members of the basis vector set if the residual vector for the pixel has a magnitude exceeding a predetermined threshold, and the basis vector set may then be optimized to eliminate one of the members of the basis vector set, whereby the optimized basis vector set includes the number of members. A system configured to perform the method may also be provided.

Abstract: A method for reducing dimensionality of hyperspectral images may include receiving a hyperspectral image having a plurality of pixels. A basis vector set including a number of members may then be established, wherein each of the members comprises a basis vector. For each of the plurality of pixels, a spectral vector for the pixel may be read and decomposed with the members of the basis vector set to derive a residual vector for the pixel. A basis vector for the pixel may then be added to the members of the basis vector set if the residual vector for the pixel has a magnitude exceeding a predetermined threshold, and the basis vector set may then be optimized to eliminate one of the members of the basis vector set, whereby the optimized basis vector set includes the number of members. A system configured to perform the method may also be provided.

Abstract: A method for reducing dimensionality of hyperspectral images may include receiving a hyperspectral image having a plurality of pixels. A basis vector set including a number of members may then be established, wherein each of the members comprises a basis vector. For each of the plurality of pixels, a spectral vector for the pixel may be read and decomposed with the members of the basis vector set to derive a residual vector for the pixel. A basis vector for the pixel may then be added to the members of the basis vector set if the residual vector for the pixel has a magnitude exceeding a predetermined threshold, and the basis vector set may then be optimized to eliminate one of the members of the basis vector set, whereby the optimized basis vector set includes the number of members. A system configured to perform the method may also be provided.

Abstract: A hyperspectral imaging sensor and an adaptive spatial spectral processing filter capable of detecting, identifying, and/or classifying targets having a spatial extent of one pixel or less includes a sensor that may be oversampled such that a pixel is spatially smaller than the optical blur or point spread function of the sensor. Adaptive spatial spectral processing may be performed on hyperspectral image data to detect targets having spectral features that are known a priori, and/or that are anomalous compared to nearby pixels. Further, the adaptive spatial spectral processing may recover target energy spread over multiple pixels and reduce background clutter to increase the signal-to-noise ratio.

Abstract: A method for reducing dimensionality of hyperspectral images may include receiving a hyperspectral image having a plurality of pixels. A basis vector set including a number of members may then be established, wherein each of the members comprises a basis vector. For each of the plurality of pixels, a spectral vector for the pixel may be read and decomposed with the members of the basis vector set to derive a residual vector for the pixel. A basis vector for the pixel may then be added to the members of the basis vector set if the residual vector for the pixel has a magnitude exceeding a predetermined threshold, and the basis vector set may then be optimized to eliminate one of the members of the basis vector set, whereby the optimized basis vector set includes the number of members. A system configured to perform the method may also be provided.

Abstract: A hyperspectral imaging sensor and an adaptive spatial spectral processing filter capable of detecting, identifying, and/or classifying targets having a spatial extent of one pixel or less includes a sensor that may be oversampled such that a pixel is spatially smaller than the optical blur or point spread function of the sensor. Adaptive spatial spectral processing may be performed on hyperspectral image data to detect targets having spectral features that are known a priori, and/or that are anomalous compared to nearby pixels. Further, the adaptive spatial spectral processing may recover target energy spread over multiple pixels and reduce background clutter to increase the signal-to-noise ratio.