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  • 1
    Publication Date: 2019-06-28
    Description: A method for utilizing both spectral and spatial redundancy in compacting and preclassifying images is presented. In multispectral satellite images, a high correlation exists between neighboring image points which tend to occupy dense and restricted regions of the feature space. The image is divided into windows of the same size where the clustering is made. The classes obtained in several neighboring windows are clustered, and then again successively clustered until only one region corresponding to the whole image is obtained. By employing this algorithm only a few points are considered in each clustering, thus reducing computational effort. The method is illustrated as applied to LANDSAT images.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: E82-10081 , NASA-CR-168402 , NAS 1.26:168402 , INPE-2170-RPE/386
    Format: application/pdf
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  • 2
    Publication Date: 2019-07-13
    Description: The use of satellite images obtained from various dates is essential for crop forecast systems. In order to make possible a multitemporal analysis, it is necessary that images belonging to each acquisition have pixel-wise correspondence. A system developed to obtain, register and record image segments from LANDSAT images in computer compatible tapes is described. The translational registration of the segments is performed by correlating image edges in different acquisitions. The system was constructed for the Burroughs B6800 computer in ALGOL language.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: E83-10373 , NASA-CR-172917 , NAS 1.26:172917 , INPE-2785-PRE/351 , Encontro Nacl. de Automatica Soc. Brasil. de Mat.; Jul 06, 1983 - Jul 13, 1983; Belem, Para
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  • 3
    Publication Date: 2019-07-13
    Description: A classification procedure for assessing crop areal proportion in multispectral scanner image is discussed. The procedure is into four parts: labeling; classification; proportion estimation; and evaluation. The procedure also has the following characteristics: multitemporal classification; the need for a minimum field information; and verification capability between automatic classification and analyst labeling. The processing steps and the main algorithms involved are discussed. An outlook on the future of this technology is also presented.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: E83-10065 , NASA-CR-169576 , NAS 1.26:169576 , INPE-2482-PRE/166 , Reuniao da SBPC; Jul 06, 1982 - Jul 14, 1982; Campinas, Sao Paulo; Brazil
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