Abstract: A system may include a memory and a processor cooperating therewith to obtain geospatially registered first and second interferometric synthetic aperture radar (IFSAR) images of a geographic area having respective first and second actual grazing angles with a difference therebetween, and convert the first IFSAR image to a modified first IFSAR image having a modified first grazing angle based upon known terrain elevation data for the geographic area. The modified first grazing angle may be closer to the second actual grazing angle than the first actual grazing angle. The processor may further recover updated terrain elevation data for the geographic area based upon the modified first IFSAR image and the second IFSAR image.

Abstract: An artificial intelligence (AI) system for geospatial height estimation may include a memory and a processor cooperating therewith to store a plurality of labeled predicted electro-optic (EO) image classified objects having respective elevation values associated therewith in a semantic label database, and train a model using trained EO imagery and the semantic label database. The processor may further estimate height values within new EO imagery for a geographic area based upon the trained model, and generate an estimated height map for the geographic area from the estimated height values and output the estimated height map on a display.

Abstract: An artificial intelligence (AI) system for generating a digital surface model (DSM) may include a memory and a processor cooperating therewith to determine an estimated height map from electro-optic (EO) imagery of a geographic area using artificial intelligence. The processor may further generate cost coefficients for a three-dimensional (3D) cost cube based upon stereo-geographic image data and height value seeding using the estimated height map, and generate a DSM for the geographic area based upon the 3D cost cube and outputting the DSM to a display.

Abstract: An artificial intelligence (AI) system for generating a digital surface model (DSM) may include a memory and a processor cooperating therewith to determine an estimated height map from electro-optic (EO) imagery of a geographic area using artificial intelligence. The processor may further generate cost coefficients for a three-dimensional (3D) cost cube based upon stereo-geographic image data and height value seeding using the estimated height map, and generate a DSM for the geographic area based upon the 3D cost cube and outputting the DSM to a display.

Abstract: A system may include a memory and a processor cooperating therewith to obtain geospatially registered first and second interferometric synthetic aperture radar (IFSAR) images of a geographic area having respective first and second actual grazing angles with a difference therebetween, and convert the first IFSAR image to a modified first IFSAR image having a modified first grazing angle based upon known terrain elevation data for the geographic area. The modified first grazing angle may be closer to the second actual grazing angle than the first actual grazing angle. The processor may further recover updated terrain elevation data for the geographic area based upon the modified first IFSAR image and the second IFSAR image.

Abstract: A system may include a memory and a processor cooperating therewith to obtain geospatial image data from a plurality of different types of sensors and generate a three-dimensional (3D) geospatial model therefrom. The processor may further determine a reference image within the 3D geospatial model based upon synthetically positioning an image sensor within the 3D geospatial model, and perform change detection between a collected image and the reference image based upon semantic change detection using deep learning.

Abstract: A method and system for producing a personal ornamental product and a custom 3D color or monochrome sculpture incorporated into the ornamental product as a keepsake ornament. The method includes the collecting three-dimensional data from one or more sensors, adjusting the 2½D or 3D data, merging the 2½D or 3D data to produce a 2½D or 3D model, rectifying the model, preparing the sculpture for building, combining the sculpture with a bulb, producing the product with a rapid prototype device, and finishing the product. The ornamental product may be composite, porcelain, ceramic, glass, metal, etc. and often takes the form of a Christmas Tree Ornament but also may be very large and designed for table top display or otherwise.

Abstract: A complex image is apodized to suppress sidelobes. An original complex image of an object is received. The complex image comprises a plurality of data points and sidelobes. The complex image is transformed to a k-space image which is then trimmed to remove all points outside of a geometric shape. This trimming is done with the shape overlaying the image and being at a first angle with respect to the image. The trimming produces a trimmed k-space image. The trimmed k-space image is then converted back to a new complex image having a sidelobe structure different from the original complex image. The new complex image is then normalized by adjusting its intensity such that its peak amplitude matches a peak amplitude in the original complex image. A minimum function is then performed on the magnitudes of the original and new complex images. The result is an apodized image with suppressed sidelobe structure.