Książka Using Multiple Robust Parameter Design Techniques to Improve Hyperspectral Anomaly Detection Algorithm Performance Matthew T Davis

Using Multiple Robust Parameter Design Techniques to Improve Hyperspectral Anomaly Detection Algorithm Performance

Język: Angielski
Oprawa: Miękka
Wydawca: Biblioscholar
Dostępność: Dostępna u dostawcy
Wysyłamy za 9-15 dni
231.31
Detecting and identifying objects of interest is the goal of all remote sensing. New advances, speci...

Informacje o książce

Język
Angielski
Oprawa
Książka - Miękka
Data wydania
2012
strony
160
EAN
9781288229970
ISBN
9781288229970
Enbook ID
08239730
Wydawca
Waga
299
Wymiary
189 x 246 x 9

Pełny opis

Detecting and identifying objects of interest is the goal of all remote sensing. New advances, specifically in hyperspectral imaging technology have provided the analyst with immense amounts of data requiring evaluation. Several filtering techniques or anomaly detection algorithms have been proposed. However, most new algorithms are insuciently verified to be robust to the broad range of hyperspectral data being made available. One such algorithm, AutoGAD, is tested here via two separate robust parameter design techniques to determine optimal parameters for consistent performance on a range of data with large attribute variances. Additionally, the results of the two techniques are compared for overall e ectiveness. The results of the test as well as optimal parameters for AutoGAD are presented and future research e orts proposed.

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