Land cover classification in rugged areas using simulated moderateresolution remote sensor data and an artificial neural network |
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| Authors: | S R Yool |
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| Affiliation: | 1. State Key Laboratory of Remote Sensing Science , Jointly Sponsored by the Institute of Remote Sensing Applications , Chinese Academy of Sciences , and Beijing Normal University , PO Box 9718, Beijing, 100101, PR China;2. Graduate School , Chinese Academy of Sciences , Beijing, 100049, PR China llwa_irsa@yahoo.com;4. State Key Laboratory of Remote Sensing Science , Jointly Sponsored by the Institute of Remote Sensing Applications , Chinese Academy of Sciences , and Beijing Normal University , PO Box 9718, Beijing, 100101, PR China;5. Graduate School , Chinese Academy of Sciences , Beijing, 100049, PR China |
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| 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. |
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