An incremental-learning neural network for the classification of remote-sensing images |
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| Affiliation: | 1. Department of Chemical and Materials Engineering, Tunghai University, No. 1727, Sec.4, Taiwan Boulevard, Taichung, Taiwan, Republic of China;2. Department of Chemical Engineering, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu, Taiwan, Republic of China;1. Beijing Normal University-Hong Kong Baptist University United International College, 2000 Jintong Road, Tangjiawan, Zhuhai, Guangdong Province, China;2. Department of Mathematics, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR, China |
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| Abstract: | A novel classifier for the analysis of remote-sensing images is proposed. Such a classifier is based on Radial Basis Function (RBF) neural networks and relies on an incremental-learning technique. This technique allows the periodical acquisition of new information whenever a new training set becomes available, while preserving the knowledge learnt by the network on previous training sets. In addition, in each retraining phase, the network architecture is automatically updated so that new classes may be considered. These characteristics make the proposed neural classifier a promising tool for several remote-sensing applications. |
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