https://doi.org/10.1117/12.339824
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Support vector machines for hyperspectral remote sensing classification
Tập 3584 - Trang 221-232 - 1999
The Support Vector Machine provides a new way to design classification
algorithms which learn from examples (supervised learning) and generalize when
applied to new data. We demonstrate its success on a difficult classification
problem from hyperspectral remote sensing, where we obtain performances of 96%,
and 87% correct for a 4 class problem, and a 16 class problem respectively.
These results ar...