USGS · 70139721
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Harold E. Lockwood. 1982. Remote sensing and digital image processing. https://pubs.usgs.gov/publication/70139721
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The technical sophistication of photoelectronic transducers, integrated circuits, and laser-beam film recorders has made digital imagery an alternative to traditional analog imagery for remote sensing. Because a digital image is stored in discrete digital values, image enhancement is possible before the data are converted to a photographic image. To create a special film-reproduction curve - which can simulate any desired gamma, relative film speed, and toe/shoulder response - the digital-to-analog transfer function of the film recorder is uniquely defined and implemented by a lookup table in the film recorder. Because the image data are acquired in spectral bands, false-color composites also can be given special characteristics by selecting a reproduction curve tailored for each band.
Muitispectral remotely sensed images have been treated as arbitrary multivariate spectral data for purposes of clustering and classifying. However, the spatial properties of image data can also be exploited. AMOEBA is a clustering and classification method that is based on a spatially derived model for image data. In an evaluation test, Landsat data were classified with both AMOEBA and a widely used spectral classifier. The test showed that irrigated crop types can be classified as accurately with the AMOEBA method as with the generally used spectral method ISOCLS; the AMOEBA method, however, requires less computer time.
Scientists at the U.S. Geological Survey's Earth Resources Observation systems (EROS) Data Center have recently completed two land-cover mapping projects in which digital terrain data were used to refine Landsat classification results. Digital ter rain data were incorporated into the Landsat classification process using two different procedures that required developing decision criteria either subjectively or quantitatively. The subjective procedure was used in a vegetation mapping project in Arizona, and the quantitative procedure was used in a forest-fuels mapping project in Montana. By incorporating digital terrain data into the Landsat classification process, more spatially accurate landcover maps were produced for both projects.