Unsupervised remote sensing image classification using an artificial immune network

Abstract

In this article, the artificial immune network (aiNet) model, a computational intelligent approach based on artificial immune networks (AINs), is applied to remote sensing image processing to improve its intelligence. aiNet has been utilized for clustering, optimization, and data analysis. Nevertheless, due to the inherent complexity of the aiNet algorithm and the large volume of data in remote sensing imagery, the application of aiNet to remote sensing image classification has been rather limited. This article presents an unsupervised artificial immune network for remote sensing image classification (RSUAIN) based on aiNet. The proposed method can adaptively obtain some user-defined parameters, such as clone rate and mutation rate, and evolve the memorial immune network by immune operators and biological properties, such as clone, mutation and memory operators, using the remote sensing image for the task of remote sensing image clustering. Three experiments with different types of images were performed to evaluate the performance of the proposed algorithm and to compare it with other traditional unsupervised classification algorithms, for example, k-means, ISODATA (Iterative Self-organizing Data Anaysis Techniques Algorithm) and fuzzy k-means. RSUAIN was observed to outperform the traditional algorithms in the three experiments and hence potentially provides an effective option for unsupervised remote sensing image classification.

Acknowledgements

The authors thank the editor and the two anonymous reviewers. Their insightful suggestions have significantly improved this article. This work is supported by the Major State Basic Research Development Programme (973 Programme) of China under Grant No. 2009CB723905, the 863 High Technology Programme of the People's Republic of China under Grant No. 2009AA12Z114, the National Natural Science Foundation of China under Grant Nos 40901213 and 40930532, the Foundation for Authors of National Excellent Doctoral Dissertations (FANEDD) of the People's Republic of China under Grant No. 201052, the Research Fund for the Doctoral Programme of Higher Education of China under Grant No. 200804861058, the Programme for New Century Excellent Talents in University under Grant No. NECT-10-0624, the Natural Science Foundation of Hubei Province under Grant No. 2009CDB173, the Fundamental Research Funds for the Central Universities under Grant No. 3103006 and the Foundation of the National Laboratory of Pattern Recognition and funded by the Key Laboratory of Geo-informatics of the State Bureau of Surveying and Mapping.