Geography

An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA

Document Type

Article

Abstract

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.

Publication Title

GIScience and Remote Sensing

Publication Date

2012

Volume

49

Issue

5

First Page

623

Last Page

643

ISSN

1548-1603

DOI

10.2747/1548-1603.49.5.623

Keywords

accuracy assessment, algorithm, calibration, forest cover, image classification, land cover, performance assessment, remote sensing

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