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Media type:
E-Article
Title:
Forest Change Detection Applying Landsat Thematic Mapper Difference Features: A Comparison of Different Classifiers in Boreal Forest Conditions
Contributor:
Heikkonen, Jukka;
Varjo, Jari
imprint:
Oxford University Press (OUP), 2004
Published in:Forest Science
Language:
English
DOI:
10.1093/forestscience/50.5.579
ISSN:
0015-749X;
1938-3738
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title>
<jats:p>This article addresses the problem of detecting forest changes via multitemporal Landsat Thematic Mapper (TM) imagery. A stand-level classification approach is selected, where, for each stand, a total of 35 statistical differences is extracted from Landsat TM images. Three forest stand-changeclasses are considered: 1) no change, 2) moderate change, and 3) considerable change. The classification results are reported by using the following classifiers: K-nearest-neighbor, Maximum Likelihood classifier with Gaussian- and Kernel-based class probability density estimation, Classificationand Regression Trees, Multilayer Perceptron (MLP) early stop committee, and the MLP with weight decay training. Two Bayesian learning methods for MLP are also used: The Evidence Framework of MacKay, and Hybrid Monte Carlo (HMC) method following Neal. The best overall correct classificationresult (88.1%) is obtained by MLP trained with HMC and the Automatic Relevance Detection approach, but the variation of the performance of the classifiers is rather small. FOR. SCI. 50(5):579–588.</jats:p>