National Technology & Engineering Solutions of Sandia, LLC

Sandia Corporation

Katherine M Simonson

David Nikolaus Perkins

Ivan Lizarraga

Tian J Ma

Roger Derek West

Matthew Strosnick

Jeffrey A Brooks

David A Torgesen

Described herein are various technologies relating to constructing a differenced change product (DCP) image. A plurality of synthetic aperture radar (SAR) images of a scene are generated based upon radar signals directed towards and reflected off of the scene, and a plurality of coherence change detection (CCD) images of the scene are generated based upon the SAR images. The CCD images are registered with one another, and their pixel values re-scaled according to a monotonic mapping function. The DCP image is generated based upon a computed pixel-wise difference between a pair of the re-scaled CCD images. The DCP image identifies locations in the scene where human activity-induced change is likely to have occurred between a pair of SAR passes of the scene.

Apparatus, system and method for highlighting activity-induced change in multi-pass synthetic aperture radar imagery

Described herein are various technologies relating to constructing a statistically-normalized coherence (SNC) image. A plurality of synthetic aperture radar (SAR) images of a scene are generated based upon radar signals directed towards and reflected off of the scene, and a plurality of coherence change detection (CCD) images of the scene are generated based upon the SAR images. The CCD images are registered with one another, and mean and variance are computed on a pixel-wise basis. A new CCD image is subsequently received, and registered with the plurality of CCD images. The SNC image is generated based upon the computed mean and variance values for each pixel in the registered CCD images, and further based upon values of pixels in the new CCD image. The SNC image identifies locations in the scene where anomalous activity is represented in the new CCD image.

Apparatus, system and method for highlighting anomalous change in multi-pass synthetic aperture radar imagery

The various technologies presented herein relate to detecting small moving entities or targets in radar imagery. Two SAR images can be captured for a common scene, wherein the scene is imaged twice from the same flight path. The first image is captured at a first instance and the second image is captured at a second instance, and differences between the two images are determined using a complex SAR change measure, excess coherency factor or DeltaC, based in part upon quantification of incoherent (or magnitude) change between the two images. A plurality of operations are performed to enable extraction of coherent change measures relating to the small moving entities from measures relating to large objects, stationary reflective structures, radar focusing artifacts, etc.

Small mover target detection in synthetic aperture radar imagery

A technique for detecting changes in a scene perceived by a staring sensor is disclosed. The technique includes acquiring a reference image frame and a current image frame of a scene with the staring sensor. A raw difference frame is generated based upon differences between the reference image frame and the current image frame. Pixel error estimates are generated for each pixel in the raw difference frame based at least in part upon spatial error estimates related to spatial intensity gradients in the scene. The pixel error estimates are used to mitigate effects of camera jitter in the scene between the current image frame and the reference image frame.

Estimating pixel variances in the scenes of staring sensors

In an image registration method, edges are detected in a first image and a second image. A percentage of edge pixels in a subset of the second image that are also edges in the first image shifted by a translation is calculated. A best registration point is calculated based on a maximum percentage of edges matched. In a predefined search region, all registration points other than the best registration point are identified that are not significantly worse than the best registration point according to a predetermined statistical criterion.

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Cedar Crest, NM, United States of America

Katherine M Simonson