An evaluation of fuzzy and texture-based classification approaches for mapping regenerating tropical forest classes from landsat-TM data

G. Palubinskas, R. M. Lucas, G. M. Foody, P. J. Curran

Research output: Contribution to journalArticlepeer-review

Abstract

Two classification approaches were investigated for the mapping of tropical forests from Landsat-TM data of a region north of Manaus in the Brazilian state of Amazonas. These incorporated textural information and made use of fuzzy approaches to classification. In eleven class classifications the texture-based classifiers (based on a Markov random field model) consistently provided higher classification accuracies than conventional per-pixel maximum likelihood and minimum distance classifications, indicating that they are more able to characterize accurately several regenerating forest classes. Measures of the strength of class memberships derived from three classification algorithms (based on the probability density function, a posteriori probability and the Mahalanobis distance) could be used to derive fuzzy image classifications and be used in post-classification processing. The latter, involving either the summation of class memberships over a local neighbourhood or the application of homogeneity measures, were found to increase classification accuracy by some 10 per cent in comparison with a conventional maximum likelihood classification, a result of comparable accuracy to that derived from the texture-based classifications.
Original languageEnglish
Pages (from-to)747-759
Number of pages13
JournalInternational Journal of Remote Sensing
Volume16
Issue number4
DOIs
Publication statusPublished - Mar 1995
Externally publishedYes

Fingerprint

Dive into the research topics of 'An evaluation of fuzzy and texture-based classification approaches for mapping regenerating tropical forest classes from landsat-TM data'. Together they form a unique fingerprint.

Cite this