@article {Yool:1998:0143-1161:85,
title = "Land cover classification in rugged areas using simulated moderateresolution remote sensor data and an artificial neural network",
journal = "International Journal of Remote Sensing",
parent_itemid = "infobike://tandf/tres",
publishercode ="tandf",
year = "1998",
volume = "19",
number = "1",
publication date ="1998-01-10T00:00:00",
pages = "85-96",
itemtype = "ARTICLE",
issn = "0143-1161",
eissn = "1366-5901",
url = "https://www.ingentaconnect.com/content/tandf/tres/1998/00000019/00000001/art00009",
doi = "doi:10.1080/014311698216440",
author = "Yool, S. R.",
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.",
}