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Supervised classification plays a key role in terms of accurate analysis of hyperspectral images. Many applications can greatly benefit from the wealth of spectral and spatial information provided by these kind of data, including land-use and land-cover mapping. Conventional classifiers treat hyperspectral images as a list of spectral measurements and do not consider spatial dependencies of the adjacent...
We examined the ability of hyper spectral (80 bands), spatial (0.3 m), and temporal (10 dates during the growing season) imagery to detect leafy spurge infestations and classify plant densities. Random forest classification was used for all analyses. Single date classifications were similar to the best classifications in other studies (73% to 90% overall accuracies), although with greater distinction...
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