Abstract: A fully-automated computer-implemented system and method for generating a road network map from a remote sensing (RS) image in which the classification accuracy is satisfactory combines moving vehicle detection with spectral classification to overcome the limitations of each. Moving vehicle detections from an RS image are used as seeds to extract and characterize image-specific spectral roadway signatures from the same RS image. The RS image is then searched and the signatures matched against the scene to grow a road network map. The entire process can be performed using the radiance measurements of the scene without having to perform the complicated geometric and atmospheric conversions, thus improving computational efficiency, the accuracy of moving vehicle detection (location, speed, heading) and ultimately classification accuracy.

Abstract: A fully-automated computer-implemented system and method for generating a road network map from a remote sensing (RS) image in which the classification accuracy is satisfactory combines moving vehicle detection with spectral classification to overcome the limitations of each. Moving vehicle detections from an RS image are used as seeds to extract and characterize image-specific spectral roadway signatures from the same RS image. The RS image is then searched and the signatures matched against the scene to grow a road network map. The entire process can be performed using the radiance measurements of the scene without having to perform the complicated geometric and atmospheric conversions, thus improving computational efficiency, the accuracy of moving vehicle detection (location, speed, heading) and ultimately classification accuracy.

Abstract: A real-time ablation sensor uses an optical detector, such as a spectrometer or radiometer, to detect ablation of a material, for example by detecting a signal indicative of ablation of the material, which may be an engineered material. The optical detector may detect reflected light, either from the material being ablated, or from products of the ablation, such as in the vicinity of the material being ablated. A light source may be used to provide light that is reflected by the material and/or the ablation products, with the reflected light received by the detector. The light may be of a selected wavelength or wavelengths, with the selection made in combination with the selection/configuration of the material to be ablated, and/or the selection/configuration of the optical detector.

Abstract: A real-time ablation sensor uses an optical detector, such as a spectrometer or radiometer, to detect ablation of a material, for example by detecting a signal indicative of ablation of the material, which may be an engineered material. The optical detector may detect reflected light, either from the material being ablated, or from products of the ablation, such as in the vicinity of the material being ablated. A light source may be used to provide light that is reflected by the material and/or the ablation products, with the reflected light received by the detector. The light may be of a selected wavelength or wavelengths, with the selection made in combination with the selection/configuration of the material to be ablated, and/or the selection/configuration of the optical detector.

Abstract: An angularly-dependent reflectance of a surface of an object is measured. Images are collected by a sensor at different sensor geometries and different light-source geometries. A point cloud is generated. The point cloud includes a location of a point, spectral band intensity values for the point, an azimuth and an elevation of the sensor, and an azimuth and an elevation of a light source. Raw pixel intensities of the object and surroundings of the object are converted to a surface reflectance of the object using specular array calibration (SPARC) targets. A three-dimensional (3D) location of each point in the point cloud is projected back to each image using metadata from the plurality of images, and spectral band values are assigned to each value in the point cloud, thereby resulting in a multi-angle spectral reflectance data set.

Abstract: A system and method for processing a daytime IR image to discriminate between solar glints and hotspots, where the latter represent man-made activity. Two spectrally distinct thermal wavelength bands are defined and respective spectral intensities are detected for a corresponding pixel in an image. A figure of merit is calculated as a function of the detected spectral intensities. The calculated figure of merit is compared to a predetermined rule to determine whether the corresponding pixel is a glint or a hotspot.

Abstract: An angularly-dependent reflectance of a surface of an object is measured. Images are collected by a sensor at different sensor geometries and different light-source geometries. A point cloud is generated. The point cloud includes a location of a point, spectral band intensity values for the point, an azimuth and an elevation of the sensor, and an azimuth and an elevation of a light source. Raw pixel intensities of the object and surroundings of the object are converted to a surface reflectance of the object using specular array calibration (SPARC) targets. A three-dimensional (3D) location of each point in the point cloud is projected back to each image using metadata from the plurality of images, and spectral band values are assigned to each value in the point cloud, thereby resulting in a multi-angle spectral reflectance data set.

Abstract: A system and method of generating point clouds from passive images. Image clusters are formed, wherein each image cluster includes two or more passive images selected from a set of passive images. Quality of the point cloud that could be generated from each image cluster is predicted for each image cluster based on a performance prediction score for each image cluster. A subset of image clusters is selected for further processing based on their performance prediction scores. A mission-specific quality score is determined for each point cloud generated and the point cloud with the highest quality score is selected for storage.

Abstract: In one example of a method for remote identifying a non-Lambertian target material, a spectral signature for a target is determined from each of at least two different sets of imagery acquired at different angles, and compared to a predicted signature for a candidate material for each of the at least two different angles. The predicted signatures take into account the known anisotropy of reflectance, and thus also radiance, of the candidate material.

Abstract: A system and method of generating point clouds from passive images. Image clusters are formed, wherein each image cluster includes two or more passive images selected from a set of passive images. Quality of the point cloud that could be generated from each image cluster is predicted for each image cluster based on a performance prediction score for each image cluster. A subset of image clusters is selected for further processing based on their performance prediction scores. A mission-specific quality score is determined for each point cloud generated and the point cloud with the highest quality score is selected for storage.

Abstract: In one example of a method for remote identifying a non-Lambertian target material, a spectral signature for a target is determined from each of at least two different sets of imagery acquired at different angles, and compared to a predicted signature for a candidate material for each of the at least two different angles. The predicted signatures take into account the known anisotropy of reflectance, and thus also radiance, of the candidate material.

Abstract: In one example of a method for remote identifying a non-Lambertian target material, a spectral signature for a target is determined from each of at least two different sets of imagery acquired at different angles, and compared to a predicted signature for a candidate material for each of the at least two different angles. The predicted signatures take into account the known anisotropy of reflectance, and thus also radiance, of the candidate material.

Abstract: In accordance with the present disclosure, a computer implemented system and method predicts the performance for a remote material identification process under real conditions and uncertainties. The method and system transforms data representing measured reflectance values for candidate materials based on environmental conditions, and uncertainties regarding the environmental conditions and/or calibration of sensors measuring radiance values into the performance predictions for a material identification process operating under those conditions and uncertainties. The performance predictions can be communicated to a designer of, for example, a multi-angle material identification system for use in selecting and setting up the system, or communicated to a consumer of images captured by the material identification system for use in interpreting results of application of the material identification process to real imagery acquired with remote sensors.

Abstract: The system relates to a method for enhancing an image by extracting edge points from a corresponding image and overlaying the edge points on the image to be enhanced. In one aspect of the method, the first image is of a lower resolution than the second image. The method also contemplates enhancing an image taken with a first modality with a second image taken with another modality.

Abstract: In one example of a method for remote identifying a non-Lambertian target material, a spectral signature for a target is determined from each of at least two different sets of imagery acquired at different angles, and compared to a predicted signature for a candidate material for each of the at least two different angles. The predicted signatures take into account the known anisotropy of reflectance, and thus also radiance, of the candidate material.

Abstract: In accordance with the present disclosure, a computer implemented system and method predicts the performance for a remote material identification process under real conditions and uncertainties. The method and system transforms data representing measured reflectance values for candidate materials based on environmental conditions, and uncertainties regarding the environmental conditions and/or calibration of sensors measuring radiance values into the performance predictions for a material identification process operating under those conditions and uncertainties. The performance predictions can be communicated to a designer of, for example, a multi-angle material identification system for use in selecting and setting up the system, or communicated to a consumer of images captured by the material identification system for use in interpreting results of application of the material identification process to real imagery acquired with remote sensors.

Abstract: The system relates to a method for enhancing an image by extracting edge points from a corresponding image and overlaying the edge points on the image to be enhanced. In one aspect of the method, the first image is of a lower resolution than the second image. The method also contemplates enhancing an image taken with a first modality with a second image taken with another modality.

Abstract: Methods, systems and apparatus for photo-processing of fluids, particularly complex fluids, such as blood products, pharmaceuticals, injectables and vaccines, are provided. The disclosed methods and systems employ non-laser light source(s) to generate monochromatic light energy, preferably in the range of 260 nm to 310 nm, for fluid treatment. Advantageous processing regimens and/or adjunct additives and/or agents may also be used to achieve desired and/or enhanced results, e.g., inactivation of pathogens, bacteria and/or viruses, modulation of immune response, and/or leukoreduction. Particularly preferred embodiments include novel temperature control systems and geometric/structural arrangements that provide enhanced processing results and/or efficiencies.

Abstract: Methods, systems and apparatus for photo-processing of fluids, particularly complex fluids, such as blood products, pharmaceuticals, injectables and vaccines, are provided. The disclosed methods and systems employ non-laser light source(s) to generate monochromatic light energy, preferably in the range of 260 nm to 310 nm, for fluid treatment. Advantageous processing regimens and/or adjunct additives and/or agents may also be used to achieve desired and/or enhanced results, e.g., inactivation of pathogens, bacteria and/or viruses, modulation of immune response, and/or leukoreduction. Particularly preferred embodiments include novel temperature control systems and geometric/structural arrangements that provide enhanced processing results and/or efficiencies.

Abstract: A halogenated hydrocarbon (HHC) detector is formed from a silent discharge (also called a dielectric barrier discharge) plasma generator. A silent discharge plasma device receives a gas sample that may contain one or more HHCs and produces free radicals and excited electrons for oxidizing the HHCs in the gas sample to produce water, carbon dioxide, and an acid including halogens in the HHCs. A detector is used to sensitively detect the presence of the acid. A conductivity cell detector combines the oxidation products with a solvent where dissociation of the acid increases the conductivity of the solvent. The conductivity cell output signal is then functionally related to the presence of HHCs in the gas sample. Other detectors include electrochemical cells, infrared spectrometers, and negative ion mobility spectrometers.