Autonomous image data reduction by analysis and interpretation
Identifieur interne : 000915 ( Main/Exploration ); précédent : 000914; suivant : 000916Autonomous image data reduction by analysis and interpretation
Auteurs : Susan Eberlein [États-Unis] ; Gigi Yates [États-Unis] ; Niles Ritter [États-Unis]Source :
- Telematics and Informatics [ 0736-5853 ] ; 1988.
Abstract
Abstract: Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise several hundred bytes. We are developing an autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover. The completed system will be capable of interpreting and fusing image data from multiple instruments to produce reduced representations of the images, and of making decisions regarding the importance of data based on current scientific goals. Analysis techniques emphasize artificial neural networks for geological classification of the multispectral images. Program output is a compact description of each region in the image including location, distance from the viewer, color, geographical classifications and other appropriate sensor information.
Url:
DOI: 10.1016/S0736-5853(88)80027-3
Affiliations:
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Compression of image data is required due to the limited bandwidth of the transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise several hundred bytes. We are developing an autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover. The completed system will be capable of interpreting and fusing image data from multiple instruments to produce reduced representations of the images, and of making decisions regarding the importance of data based on current scientific goals. Analysis techniques emphasize artificial neural networks for geological classification of the multispectral images. Program output is a compact description of each region in the image including location, distance from the viewer, color, geographical classifications and other appropriate sensor information.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country></list><tree><country name="États-Unis"><noRegion><name sortKey="Eberlein, Susan" sort="Eberlein, Susan" uniqKey="Eberlein S" first="Susan" last="Eberlein">Susan Eberlein</name></noRegion><name sortKey="Ritter, Niles" sort="Ritter, Niles" uniqKey="Ritter N" first="Niles" last="Ritter">Niles Ritter</name><name sortKey="Yates, Gigi" sort="Yates, Gigi" uniqKey="Yates G" first="Gigi" last="Yates">Gigi Yates</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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