ANN Based High Spatial Resolution Remote Sensing Wetland Classification

RS (Remote Sensing) image classification based on ANN (Artificial Neural Network) is carried out with high spatial resolution images of the wetland, which is the most important ecological environment element within the land components. Wetland dynamic change monitoring is often built upon its classi...

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Bibliographic Details
Published in2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) pp. 180 - 183
Main Authors Ke Zun-You, An Ru, Li Xiang-Juan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2015
Subjects
Artificial Neural Network
Artificial neural networks
Biological neural networks
Buildings
Classification algorithms
Hidden Neuron Number
High Spatial Resolution Image
Neurons
Remote sensing
Wetland Classification
Wetlands
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Summary:RS (Remote Sensing) image classification based on ANN (Artificial Neural Network) is carried out with high spatial resolution images of the wetland, which is the most important ecological environment element within the land components. Wetland dynamic change monitoring is often built upon its classification result concerned here. The typical high spatial resolution image of the wetland in Nanjing is used as a study case by ANN method in comparison with MLC (Maximum Likelihood Classification). Furthermore, the optimal number of ANN hidden neurons are simulated for enhance the classification effectivity. Totally, the results show classification method of ANN with optimal hidden neurons can effectively distinguish ground objects and improve the classification accuracy. The overall accuracy of the ANN classification is up to 93% and the Kappa coefficient is over 0.89.
DOI:10.1109/DCABES.2015.52