
Proceedings Paper
Vehicle change detection from aerial imagery using detection response mapsFormat | Member Price | Non-Member Price |
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Paper Abstract
Image change detection has long been used to detect significant events in aerial imagery, such as the arrival or departure
of vehicles. Usually only the underlying structural changes are of interest, particularly for movable objects, and the
challenge is to differentiate the changes of intelligence value (change detections) from incidental appearance changes (false
detections). However, existing methods for automated change detection continue to be challenged by nuisance variations in
operating conditions such as sensor (camera exposure, camera viewpoints), targets (occlusions, type), and the environment
(illumination, shadows, weather, seasons). To overcome these problems, we propose a novel vehicle change detection
method based on the detection response maps (DRM). The detector serves as an advanced filter that normalizes the images
being compared specifically for object level change detection (OLCD). In contrast to current methods that compare pixel
intensities, the proposed DRM-OLCD method is more robust to nuisance changes and variations in image appearance. We
demonstrate object-level change detection for vehicle appearing and disappearing in electro-optical (EO) visual imagery.
Paper Details
Date Published: 19 June 2014
PDF: 11 pages
Proc. SPIE 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II, 908906 (19 June 2014); doi: 10.1117/12.2055362
Published in SPIE Proceedings Vol. 9089:
Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II
Matthew F. Pellechia; Kannappan Palaniappan; Shiloh L. Dockstader; Paul B. Deignan; Peter J. Doucette; Donnie Self, Editor(s)
PDF: 11 pages
Proc. SPIE 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II, 908906 (19 June 2014); doi: 10.1117/12.2055362
Show Author Affiliations
Zhaohui H. Sun, Kitware, Inc. (United States)
Mathew Leotta, Kitware, Inc. (United States)
Anthony Hoogs, Kitware, Inc. (United States)
Rusty Blue, Kitware, Inc. (United States)
Robert Neuroth, Air Force Research Lab. (United States)
Mathew Leotta, Kitware, Inc. (United States)
Anthony Hoogs, Kitware, Inc. (United States)
Rusty Blue, Kitware, Inc. (United States)
Robert Neuroth, Air Force Research Lab. (United States)
Juan Vasquez, Air Force Research Lab. (United States)
Amitha Perera, Kitware, Inc. (United States)
Matthew Turek, Kitware, Inc. (United States)
Erik Blasch, Air Force Research Lab. (United States)
Amitha Perera, Kitware, Inc. (United States)
Matthew Turek, Kitware, Inc. (United States)
Erik Blasch, Air Force Research Lab. (United States)
Published in SPIE Proceedings Vol. 9089:
Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II
Matthew F. Pellechia; Kannappan Palaniappan; Shiloh L. Dockstader; Paul B. Deignan; Peter J. Doucette; Donnie Self, Editor(s)
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