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Supervised Classification of Remotely Sensed Imagery Using a Modified -NN Technique

โœ Scribed by Samaniego, L.; Bardossy, A.; Schulz, K.


Book ID
114626488
Publisher
IEEE
Year
2008
Tongue
English
Weight
969 KB
Volume
46
Category
Article
ISSN
0196-2892

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Classification tree analysis (CTA) provides an effective suite of algorithms for classifying remotely sensed data, but it has the limitations of (1) not searching for optimal tree structures and (2) being adversely affected by outliers, inaccurate training data, and unbalanced data sets. Stochastic