A computer program automatically builds large, full-resolution mosaics of multispectral images of Earth landmasses from images acquired by Landsat 7, complete with matching of colors and blending between adjacent scenes. While the code has been used extensively for Landsat, it could also be used for other data sources. A single mosaic of as many as 8,000 scenes, represented by more than 5 terabytes of data and the largest set produced in this work, demonstrated what the code could do to provide global coverage. The program first statistically analyzes input images to determine areas of coverage and data-value distributions. It then transforms the input images from their original universal transverse Mercator coordinates to other geographical coordinates, with scaling. It applies a first-order polynomial brightness correction to each band in each scene. It uses a data-mask image for selecting data and blending of input scenes. Under control by a user, the program can be made to operate on small parts of the output image space, with check-point and restart capabilities. The program runs on SGI IRIX computers. It is capable of parallel processing using shared-memory code, large memories, and tens of central processing units. It can retrieve input data and store output data at locations remote from the processors on which it is executed.
This program was written by Lucian Plesea of Caltech for NASA's Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free online at www.techbriefs.com/tsp under the Software category.
This software is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (818) 393-2827. Refer to NPO-40999.
This Brief includes a Technical Support Package (TSP).
Satellite Image Mosaic Engine
(reference NPO-40999) is currently available for download from the TSP library.
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Overview
The document is a Technical Support Package for NASA's Satellite Image Mosaic Engine, developed at the Jet Propulsion Laboratory (JPL) under the Earth Image Project. It outlines the software's capabilities, development background, and operational requirements.
The Satellite Image Mosaic Engine is designed to produce large, high-resolution image mosaics from satellite data, specifically utilizing over 8,000 Landsat 7 scenes, which amounts to more than 5 terabytes of input data. The software operates in an SGI (Silicon Graphics, Inc.) environment and requires a minimum of 1GB of RAM and a single CPU for optimal performance. It is noteworthy for its ability to function without human intervention, making it a significant advancement over existing software solutions.
The document highlights that the software is still in the prototype stage and has successfully generated a 15-meter mosaic of the entire Earth within approximately 30 days. This achievement demonstrates the software's efficiency in processing vast amounts of data and its potential for various applications in remote sensing and environmental monitoring.
The development of the Satellite Image Mosaic Engine was funded by NASA, and it builds upon previously existing code from the SGI Image Vision Library. However, the software has not yet been disclosed or distributed outside of JPL, indicating that it is still in the early stages of its lifecycle.
The document also addresses the software's commercial applications, noting that there are currently no known commercial interests or applications identified by the author. This suggests that while the technology is promising, further exploration of its market potential may be necessary.
In summary, the Satellite Image Mosaic Engine represents a significant technological advancement in satellite image processing, capable of creating detailed mosaics from extensive datasets with minimal human oversight. Its development is part of NASA's broader efforts to leverage aerospace-related technologies for scientific and commercial applications. The document serves as a foundational overview of the software's capabilities, requirements, and current status, while also indicating the potential for future exploration and application in various fields.
