Eagle Technology, LLC

Glenn Boudreaux

Mark Donald Rahmes

John L Delay

William W Watkins

Donald Alan Lieb

Patrick Cowhill

Tomoka Yamada

Mark D Elstrom

Bill Watkins

David Bruce Chester

Thomas Jay Billhartz

Emile Ganthier

Harlan Yates

Chad Chung Lau

Jason Calvert

David Priebel

Jay Hermann

Joseph Hucks

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.

Change detection device and related methods

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.

Cognitive radio providing jammer type detection based upon game theoretic model and related methods

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.

Geospatial modeling system providing 3D geospatial model update based upon iterative predictive image registration and related methods

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.

System using a priori terrain height data for interferometric synthetic aperture radar (IFSAR) phase disambiguation and related methods

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.

Geospatial modeling system providing 3D geospatial model update based upon predictively registered image and related methods

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.

Artificial intelligence (AI) system and methods for generating estimated height maps from electro-optic imagery

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.

Artificial intelligence (AI) system using height seed initialization for extraction of digital elevation models (DEMs) and related methods

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.

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Melbourne, FL, United States of America

Glenn Boudreaux