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Data fusion of Landsat TM and IRS images in forest classificationAuthor(s): Guangxing Wang; Markus Holopainen; Eero Lukkarinen
Source: In: Hansen, Mark; Burk, Tom, eds. Integrated tools for natural resources inventories in the 21st century. Gen. Tech. Rep. NC-212. St. Paul, MN: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station. 654-663.
Publication Series: General Technical Report (GTR)
Station: North Central Research Station
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DescriptionData fusion of Landsat TM images and Indian Remote Sensing satellite panchromatic image (IRS-1C PAN) was studied and compared to the use of TM or IRS image only. The aim was to combine the high spatial resolution of IRS-1C PAN to the high spectral resolution of Landsat TM images using a data fusion algorithm. The ground truth of the study was based on a sample of 1,020 relascope plots. The results indicated that compared to use of TM or IRS image only, the data fusion increased the classification accuracy of four tree species classes, three age classes, and their 12 combined stand classes. However, the classification using IRS image alone was not better than that using TM images only.
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CitationWang, Guangxing; Holopainen, Markus; Lukkarinen, Eero. 2000. Data fusion of Landsat TM and IRS images in forest classification. In: Hansen, Mark; Burk, Tom, eds. Integrated tools for natural resources inventories in the 21st century. Gen. Tech. Rep. NC-212. St. Paul, MN: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station. 654-663.
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