Abstract: Embodiments can relate to a system for producing a digital image by automatically correcting for scattering and/or absorption effects in an original digital image. The system can include a processor. The system can include memory containing a computer program that when executed will cause the processor to receive radiance spectra of a digital image, and execute a Gaussian machine learning model. The system can generate a reflectance parameter by predicting a ground reflectance value based on a Gaussian probability distribution of radiance spectra. The system can solve for a conversion coefficient based on the reflectance parameter. The system can produce an altered digital image by at least one or more of removing, filtering, and/or altering data for at least one pixel of a digital image based on the conversion coefficient.

Abstract: Systems, methods and computer program products, for identification of materials based on hyperspectral imagery, are disclosed. An example system comprises one or more processors, a memory, a library of spectral signatures, a receiver, a model generator, and a material identifier. The receiver module is configured to receive a first spectral signature corresponding to a region of interest contained in the hyperspectral image. The model generator is configured to create a model search space including one or more model signatures based on the spectral signatures in the library, wherein each of the one or more model signatures approximate the first spectral signature. The material identifier is a material identifier configured to calculate a probability associated with a presence or absence of a material within the first spectral signature, based on the first spectral signature and the model search space and determine the presence or absence of the material in the region of interest based on the probability.

Abstract: A method, non-transitory computer readable medium, and apparatus that provides object-based identification, sorting and ranking of target detections includes determining a target detection score for each pixel in each of one or more images for each of one or more targets. A region around one or more of the pixels with the determined detection scores which are higher than the determined detection scores for the remaining pixels in each of the one or more of images is identified. An object based score for each of the identified regions in each of the one or more images is determined. The one or more identified regions with the determined object based score for each region is provided.

Abstract: Systems, methods and computer program products, for identification of materials based on hyperspectral imagery, are disclosed. An example system comprises one or more processors, a memory, a library of spectral signatures, a receiver, a model generator, and a material identifier. The receiver module is configured to receive a first spectral signature corresponding to a region of interest contained in the hyperspectral image. The model generator is configured to create a model search space including one or more model signatures based on the spectral signatures in the library, wherein each of the one or more model signatures approximate the first spectral signature. The material identifier is a material identifier configured to calculate a probability associated with a presence or absence of a material within the first spectral signature, based on the first spectral signature and the model search space and determine the presence or absence of the material in the region of interest based on the probability.

Abstract: A method, non-transitory computer readable medium, and apparatus that provides object-based identification, sorting and ranking of target detections includes determining a target detection score for each pixel in each of one or more images for each of one or more targets. A region around one or more of the pixels with the determined detection scores which are higher than the determined detection scores for the remaining pixels in each of the one or more of images is identified. An object based score for each of the identified regions in each of the one or more images is determined. The one or more identified regions with the determined object based score for each region is provided.