Land cover classification in rugged areas using simulated moderateresolution remote sensor data and an artificial neural network
Author: Yool, S. R.
Source: International Journal of Remote Sensing, Volume 19, Number 1, 1998 , pp. 85-96(12)
Publisher: Taylor and Francis Ltd
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.
Document Type: Research Article
Publication date: 1998-01-10