Abstract: A cognitive radio device may include a radio frequency (RF) detector, an RF transmitter having a selectable hopping frequency window, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal, determine a jammer type associated with the jammer signal from among a plurality of different jammer types based upon a game theoretic model, and operate the RF transmitter to change the selectable hopping frequency window responsive to the determined jammer type of the jammer signal.

Abstract: An object detection device may include a variational autoencoder (VAE) configured to encode image data to generate a latent vector, and decode the latent vector to generate new image data. The object detection device may also include a quantum computing circuit configured to perform quantum subset summing, and a processor. The processor may be configured to generate a game theory reward matrix for a plurality of different deep learning models, cooperate with the quantum computing circuit to perform quantum subset summing of the game theory reward matrix, select a deep learning model from the plurality thereof based upon the quantum subset summing of the game theory reward matrix, and process the new image data using the selected deep learning model for object detection.

Abstract: A change detection device may include a variational autoencoder (VAE) configured to encode image data to generate a latent vector, and decode the latent vector to generate new image data. The change detection device may further include a controller configured to select a deep learning model from different deep learning models based upon the new image data and a game theory reward matrix, and process the new image data using the selected deep learning model to detect changes therein.

Abstract: A change detection device may include a variational autoencoder (VAE) configured to encode image data to generate a latent vector, and decode the latent vector to generate new image data. The change detection device may further include a controller configured to select a deep learning model from different deep learning models based upon the new image data and a game theory reward matrix, and process the new image data using the selected deep learning model to detect changes therein.

Abstract: A cognitive radio device may include a radio frequency (RF) detector, an RF transmitter having a selectable hopping frequency window, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal, determine a jammer type associated with the jammer signal from among a plurality of different jammer types based upon a game theoretic model, and operate the RF transmitter to change the selectable hopping frequency window responsive to the determined jammer type of the jammer signal.

Abstract: A geospatial modeling system may include a memory and a processor cooperating therewith to: (a) generate a three-dimensional (3D) geospatial model including geospatial voxels based upon a plurality of geospatial images; (b) select an isolated geospatial image from among the plurality of geospatial images; (c) determine a reference geospatial image from the 3D geospatial model using Artificial Intelligence (AI) and based upon the isolated geospatial image; (d) align the isolated geospatial image and the reference geospatial image to generate a predictively registered image; (e) update the 3D geospatial model based upon the predictively registered image; and (f) iteratively repeat (b)-(e) for successive isolated geospatial images.

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 geospatial modeling system may include a memory and a processor cooperating therewith to generate a three-dimensional (3D) geospatial model including geospatial voxels based upon a plurality of geospatial images, obtain a newly collected geospatial image, and determine a reference geospatial image from the 3D geospatial model using Artificial Intelligence (AI) and based upon the newly collected geospatial image. The processor may further align the newly collected geospatial image and the reference geospatial image to generate a predictively registered image, and update the 3D geospatial model based upon the predictively registered 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: A geospatial modeling system may include a memory and a processor cooperating therewith to generate a three-dimensional (3D) geospatial model including geospatial voxels based upon a plurality of geospatial images, obtain a newly collected geospatial image, and determine a reference geospatial image from the 3D geospatial model using Artificial Intelligence (AI) and based upon the newly collected geospatial image. The processor may further align the newly collected geospatial image and the reference geospatial image to generate a predictively registered image, and update the 3D geospatial model based upon the predictively registered image.

Abstract: A geospatial modeling system may include a memory and a processor cooperating therewith to: (a) generate a three-dimensional (3D) geospatial model including geospatial voxels based upon a plurality of geospatial images; (b) select an isolated geospatial image from among the plurality of geospatial images; (c) determine a reference geospatial image from the 3D geospatial model using Artificial Intelligence (AI) and based upon the isolated geospatial image; (d) align the isolated geospatial image and the reference geospatial image to generate a predictively registered image; (e) update the 3D geospatial model based upon the predictively registered image; and (f) iteratively repeat (b)-(e) for successive isolated geospatial images.

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: 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: 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.