30 September 2003 Classification of multispectral satellite image data using improved NRBF neural networks
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Proceedings Volume 5267, Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision; (2003) https://doi.org/10.1117/12.518551
Event: Photonics Technologies for Robotics, Automation, and Manufacturing, 2003, Providence, RI, United States
Abstract
This paper describes a novel classification technique-NRBF (Normalized Radial Basis Function) neural network classifier based on spectral clustering methods. The spectral method is used in the unsupervised learning part of the NRBF neural networks. Compared with other general clustering methods used in NRBF neural networks, such as KMeans, the spectral method can avoid the local minima problem and therefore multiple restarts are not necessary to obtain a good solution. This classifier was tested with satellite multi-spectral image data of New England acquired by Landsat 7 ETM+ sensors. Classification results show that this new neural network model is more accurate and robust than the conventional RBF model. Furthermore, we analyze how the number of the hidden units affects training and testing accuracy. These results suggest that this new model may be an effective method for classification of multispectral satellite image data.
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Xiaoli Tao, Howard E. Michel, "Classification of multispectral satellite image data using improved NRBF neural networks", Proc. SPIE 5267, Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision, (30 September 2003); doi: 10.1117/12.518551; https://doi.org/10.1117/12.518551
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