
Proceedings Paper
KOLAM: a cross-platform architecture for scalable visualization and tracking in wide-area imageryFormat | Member Price | Non-Member Price |
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Paper Abstract
KOLAM is an open, cross-platform, interoperable, scalable and extensible framework supporting a novel multi-
scale spatiotemporal dual-cache data structure for big data visualization and visual analytics. This paper focuses
on the use of KOLAM for target tracking in high-resolution, high throughput wide format video also known as
wide-area motion imagery (WAMI). It was originally developed for the interactive visualization of extremely large
geospatial imagery of high spatial and spectral resolution. KOLAM is platform, operating system and (graphics)
hardware independent, and supports embedded datasets scalable from hundreds of gigabytes to feasibly petabytes
in size on clusters, workstations, desktops and mobile computers. In addition to rapid roam, zoom and hyper-
jump spatial operations, a large number of simultaneously viewable embedded pyramid layers (also referred to
as multiscale or sparse imagery), interactive colormap and histogram enhancement, spherical projection and
terrain maps are supported. The KOLAM software architecture was extended to support airborne wide-area
motion imagery by organizing spatiotemporal tiles in very large format video frames using a temporal cache of
tiled pyramid cached data structures. The current version supports WAMI animation, fast intelligent inspection,
trajectory visualization and target tracking (digital tagging); the latter by interfacing with external automatic
tracking software. One of the critical needs for working with WAMI is a supervised tracking and visualization
tool that allows analysts to digitally tag multiple targets, quickly review and correct tracking results and apply
geospatial visual analytic tools on the generated trajectories. One-click manual tracking combined with multiple
automated tracking algorithms are available to assist the analyst and increase human effectiveness.
Paper Details
Date Published: 13 June 2013
PDF: 17 pages
Proc. SPIE 8747, Geospatial InfoFusion III, 87470N (13 June 2013); doi: 10.1117/12.2018162
Published in SPIE Proceedings Vol. 8747:
Geospatial InfoFusion III
Matthew F. Pellechia; Richard J. Sorensen; Kannappan Palaniappan, Editor(s)
PDF: 17 pages
Proc. SPIE 8747, Geospatial InfoFusion III, 87470N (13 June 2013); doi: 10.1117/12.2018162
Show Author Affiliations
Joshua Fraser, Univ. of Missouri-Columbia (United States)
Anoop Haridas, Univ. of Missouri-Columbia (United States)
Guna Seetharaman, Air Force Research Lab. (United States)
Anoop Haridas, Univ. of Missouri-Columbia (United States)
Guna Seetharaman, Air Force Research Lab. (United States)
Raghuveer M. Rao, U.S. Army Research Lab. (United States)
Kannappan Palaniappan, Univ. of Missouri-Columbia (United States)
Kannappan Palaniappan, Univ. of Missouri-Columbia (United States)
Published in SPIE Proceedings Vol. 8747:
Geospatial InfoFusion III
Matthew F. Pellechia; Richard J. Sorensen; Kannappan Palaniappan, Editor(s)
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