Abstract: Systems and methods for satellite Synthetic Aperture Radar (SAR) artifact suppression for enhanced three-dimensional feature extraction, change detection, and/or visualizations are described. In some aspects, the described systems and methods include a method for suppressing artifacts from complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for creating a photo-realistic 3D model of a scene based on complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for identifying three-dimensional (3D) features and changes in SAR imagery.

Abstract: Systems and methods for satellite Synthetic Aperture Radar (SAR) artifact suppression for enhanced three-dimensional feature extraction, change detection, and/or visualizations are described. In some aspects, the described systems and methods include a method for suppressing artifacts from complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for creating a photo-realistic 3D model of a scene based on complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for identifying three-dimensional (3D) features and changes in SAR imagery.

Abstract: In some aspects, there is provided a method for determining a rotational orientation of an object in an image. The image depicts the object in a scene. The method includes providing images depicting the object in the scene to a trained statistical model. The images depict the scene of the image at different rotation angles. A rotation angle of a respective image corresponds to a potential rotational orientation of the object depicted in the respective image. The method further includes, in response to the providing, receiving, for each image, a confidence score indicating a likelihood generated by the trained statistical model that the object is at the potential rotational orientation corresponding to the rotation angle of the respective image. The method further includes determining the rotational orientation of the object in the image based at least in part on an analysis of the confidence scores and respective potential rotational orientations.

Abstract: Systems and methods for satellite Synthetic Aperture Radar (SAR) artifact suppression for enhanced three-dimensional feature extraction, change detection, and/or visualizations are described. In some aspects, the described systems and methods include a method for suppressing artifacts from complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for creating a photo-realistic 3D model of a scene based on complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for identifying three-dimensional (3D) features and changes in SAR imagery.

Abstract: Systems and methods for satellite Synthetic Aperture Radar (SAR) artifact suppression for enhanced three-dimensional feature extraction, change detection, and/or visualizations are described. In some aspects, the described systems and methods include a method for suppressing artifacts from complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for creating a photo-realistic 3D model of a scene based on complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for identifying three-dimensional (3D) features and changes in SAR imagery.

Abstract: In some aspects, there is provided a method for determining a rotational orientation of an object in an image. The image depicts the object in a scene. The method includes providing images depicting the object in the scene to a trained statistical model. The images depict the scene of the image at different rotation angles. A rotation angle of a respective image corresponds to a potential rotational orientation of the object depicted in the respective image. The method further includes, in response to the providing, receiving, for each image, a confidence score indicating a likelihood generated by the trained statistical model that the object is at the potential rotational orientation corresponding to the rotation angle of the respective image. The method further includes determining the rotational orientation of the object in the image based at least in part on an analysis of the confidence scores and respective potential rotational orientations.

Abstract: Systems and methods for satellite Synthetic Aperture Radar (SAR) artifact suppression for enhanced three-dimensional feature extraction, change detection, and/or visualizations are described. In some aspects, the described systems and methods include a method for suppressing artifacts from complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for creating a photo-realistic 3D model of a scene based on complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for identifying three-dimensional (3D) features and changes in SAR imagery.

Abstract: An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. A Hebbian learning rule may be used to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of pixel patches over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

Abstract: An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. A Hebbian learning rule may be used to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of pixel patches over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.