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| | Classification of multispectral, multitemporal, multisource /Spatial data using artificial neural networks |
 | | A particularly appealing and promising technical approach for enhanced classification is the integration of multispectral, multitemporal satellite remote sensing data and multisource ancillary data. |
 | | It has been suggested that artificial neural network applications using data from diverse sources with different distributions, such as multispectral, multitemporal remote sensing data, and image segmentation from internal /Spatial structure or GIS underlying data layers, should be explored (Civco, 1993). |
 | | These two quadrangles were chosen because of their diversity in land use and land cover, the availability of multiple Landsat TM scenes, and their use in previous image analysis and classification studies (Civco, 1991, 1993, Wang and Civco, 1992a, 1992b). |
| libraries.maine.edu /Spatial/gisweb/spatdb/acsm/ac94014.html (3936 words) |
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