Land cover classification in rugged areas using simulated moderateresolution remote sensor data and an artificial neural network

S. R. Yool

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Rugged land cover classification accuracies produced by an artificial neural network (ANN) using simulated moderate-resolution remote sensor data exceed overall accuracies produced using the maximum likelihood rule (MLR). Land cover in spatially-complex areas and at broad spatial scales may be difficult to monitor due to ambiguities in spectral reflectance information produced from cloud-related and topographic effects, or from sampling constraints. Such ambiguities may produce inconsistent estimates of changes in vegetation status, surface energy balance, run-off yields, or other land cover characteristics. By use of a 'back-classification' protocol, which uses the same pixels for testing as for training the classifier, tests of ANN versus MLR-based classifiers demonstrated the ANNbased classifier equalled or exceeded classification accuracies produced by the MLR-based classifier in five of six land cover classes evaluated.

Original languageEnglish (US)
Pages (from-to)85-96
Number of pages12
JournalInternational Journal of Remote Sensing
Volume19
Issue number1
DOIs
StatePublished - 1998

ASJC Scopus subject areas

  • General Earth and Planetary Sciences

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