Raytheon Company

Grant B Boroughs

Jody Dale Verret

Richard W Ely

Stephen J Raif

John J Coogan

Mac Allen Cody

Corey J Collard

Matthew A Schaefer

Jerry O Moore

Lisa A Mccoy

Robert T Pierce

James G Hayes

Ali J Arik

Kevin M Grange

Robert V Opalecky

Mark J Graebner

Robert M Willer

A fully-automated computer-implemented system and method for generating a road network map from a remote sensing (RS) image in which the classification accuracy is satisfactory combines moving vehicle detection with spectral classification to overcome the limitations of each. Moving vehicle detections from an RS image are used as seeds to extract and characterize image-specific spectral roadway signatures from the same RS image. The RS image is then searched and the signatures matched against the scene to grow a road network map. The entire process can be performed using the radiance measurements of the scene without having to perform the complicated geometric and atmospheric conversions, thus improving computational efficiency, the accuracy of moving vehicle detection (location, speed, heading) and ultimately classification accuracy.

Automated computer system and method of road network extraction from remote sensing images using vehicle motion detection to seed spectral classification

A computer-implemented system and method of image cross-correlation improves the sub-pixel accuracy of the correlation surface and subsequent processing thereof. One or both of the template or search windows are resampled using the fractional portions of the correlation offsets X and Y produced by the initial image cross-correlation. The resampled window is then correlated with the other original window to produce a resampled cross-correlation surface. Removing the fractional or sub-pixel offsets between the template and search windows improves the 'sameness' of the represented imagery thereby improving the quality and accuracy of the correlation surface, which in turn improves the quality and accuracy of the FOM or other processing of the correlation surface. The process may be iterated to improve accuracy or modified to generate resampled cross-correlation surfaces for multiple possible offsets and to accept the one with the most certainty.

Resampled image cross-correlation

A system receives digital images of a geographic location, associates each digital image with ground control points in a set of reference stereo images, and associates each digital image to each other digital image via image to image tiepoints. The system updates a geometry of each image via a bundle adjustment, and uses a prioritized stacking order to establish piecewise linear seam lines between each of the images. The system finally builds a prioritized map in a mosaic space specifying the source image pixels that are used in each region of the output mosaic, and forms the mosaic image using the prioritized map.

Ortho-image mosaic production system

An image processing technique uses requirements for a geospatial distribution of image tie points for a triangulation of images. The images are correlated, thereby generating candidate tie points across the images. Statistical consistency checks are applied to the images to identify and dispose of the candidate tie points that are local outliers, and a geometric identification technique is applied to the images to identify and dispose of the candidate tie points that are global outliers. The candidate tie points that are not local outliers or global outliers are spatially down-selected such that the spatially down-selected candidate tie points satisfy the one or more requirements for the geospatial distribution.

Image tiepoints

According to at least one aspect, a river velocity estimation system is provided. The river velocity estimation system includes one or more components executable by at least one processor that are configured to receive terrain information and at least one image including a river, identify an area of interest in the at least one image that includes the river, identify a course of the river and a boundary of the river based on the area of interest in the at least one image, estimate a slope of the river based on the terrain information, determine a hydraulic radius of the river based on the boundary of the river and the course of the river, and estimate a flow rate of water in the river based on at least the slope of the river and the hydraulic radius of the river.

Apparatus and processes for estimating river velocity

A system and methods can create a synthetic image of a target from a 3D data set, by using an electro-optical (EO) image and sun geometry associated with the EO image. In some examples, a 3D surface model is created from a 3D data set. The 3D surface model establishes a local surface orientation at each point in the 3D data set. A surface shaded relief (SSR) is produced from the local surface orientation, from an EO image, and from sun geometry associated with the EO image. Points in the SSR that are in shadows are shaded appropriately. The SSR is projected into the image plane of the EO image. Edge-based registration extracts tie points from the projected SSR. The 3D data set converts the tie points to ground control points. A geometric bundle adjustment aligns the EO image geometry to the 3D data set.

System and method for automatically registering an image to a three-dimensional point set

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Southlake, TX, United States of America

Grant B Boroughs