期刊
GISCIENCE & REMOTE SENSING
卷 49, 期 5, 页码 623-643出版社
TAYLOR & FRANCIS LTD
DOI: 10.2747/1548-1603.49.5.623
关键词
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资金
- Divn Of Social and Economic Sciences
- Direct For Social, Behav & Economic Scie [0849985] Funding Source: National Science Foundation
The iterative and convergent nature of ensemble learning algorithms provides potential for improving classification of complex landscapes. This study performs land-cover classification in a heterogeneous Massachusetts landscape by comparing three ensemble learning techniques (bagging, boosting, and random - forests) and a non-ensemble learning algorithm (classification trees) using multiple criteria related to algorithm and training data characteristics. The ensemble learning algorithms had comparably high accuracy (Kappa range: 0.76-0.78), which was 11% higher than that of classification trees. Ensemble learning techniques were not influenced by calibration data variability, were robust to one-fifth calibration data noise, and insensitive to a 50% reduction in calibration data size.
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