The resource limited artificial immune system (RLAIS), a new computational intelligence approach, is being increasingly recognized as one of the most competitive methods for data clustering and analysis. Nevertheless, owing to the inherent complexity of the conventional RLAIS algorithm, its application to multi/hyper-class remote sensing image classification has been considerably limited. This paper explores a novel artificial immune algorithm based on the resource limited principles for supervised multi/hyper-spectral image classification. Three experiments with different types of images were performed to evaluate the performance of the proposed algorithm in comparison with other traditional image classification algorithms: parallelepiped, minimum distance, maximum likelihood, K-nearest neighbour and back-propagation neural network. The results show that the proposed algorithm consistently outperforms the traditional algorithms in all the experiments and hence provides an effective new option for processing multi/hyper spectral remote sensing images.
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Document Type: Research Article
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, People's Republic of China
Department of Geography and Resource Management, The Chinese University of Hong Kong Shatin, NT, Hong Kong and Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing
Publication date: 2007-01-01
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