Preliminary evaluation of thematic mapper sensor characteristics relative to land cover/land use discrimination
D. L. Williams, J. R. Irons, Brian L. Markham, R. Nelson, D. L. Toll, R. S. Latty, M. L. Stauffer
Abstract
D. L. Williams, J. R. Irons, Brian L. Markham, R. Nelson, D. L. Toll, R. S. Latty, M. L. Stauffer
Abstract
Preliminary experimental results of airborne thematic mapper (TM) data taken to quantify the effect of three major TM sensor parameters, spectral, spatial, and radiometric resolution, six months after launch of Landsat-4 are reported. The flight took place on Nov. 2, 1982 over Washington, D.C., and data gathered were compared with ground reference data from color airborne photography on a 1:40,000 scale. Analyses proceeded by deleting one band from each of four data sets, thus making the data equivalent to MSS data. Attention was directed to land cover/use classes in a quick-look format. A per-pixel maximum likelihood scheme was found to increase the recognition and dicrimination categorization capabilities. Finer spatial resolution, however, impeded classification due to increased within-class variability of the field-center pixels, which also incresed class overlap in the spectral data base. Improved data analyses techniques are therefore needed to exploit the available higher spatial resolution of the TM.
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Preliminary experimental results of airborne thematic mapper (TM) data taken to quantify the effect of three major TM sensor parameters, spectral, spatial, and radiometric resolution, six months after launch of Landsat-4 are reported. The flight took place on Nov. 2, 1982 over Washington, D.C., and data gathered were compared with ground reference data from color airborne photography on a 1:40,000 scale. Analyses proceeded by deleting one band from each of four data sets, thus making the data equivalent to MSS data. Attention was directed to land cover/use classes in a quick-look format. A per-pixel maximum likelihood scheme was found to increase the recognition and dicrimination categorization capabilities. Finer spatial resolution, however, impeded classification due to increased within-class variability of the field-center pixels, which also incresed class overlap in the spectral data base. Improved data analyses techniques are therefore needed to exploit the available higher spatial resolution of the TM.
Key concepts: Thematic Mapper, Land cover, Remote sensing, Pixel, Thematic map, Image resolution, Scale (ratio), Data set