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Classification of multi-spectral remote sensing data using a local transfer function classifier
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DOI：10.1080/01431160600823222.png)
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This paper introduces a new neural network, called the local transfer function classifier (LTF-C), for classification of multi-spectral remote sensing data. The network structure of LTF-C is similar to that of the radial basis function neural network (RBF), but LTF-C utilizes an entirely different learning algorithm. In particular, the network structure of LTF-C is not predetermined, but changes dynamically during the learning. Such a learning algorithm fits well to the classification problem, and guarantees that the size of the network is as large as is needed. The classification results show that LTF-C evidently has a better classification accuracy than the six other classifiers in the experiment.
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2.6
论文数:
1.2W
被引数:
2.7W
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