Abstract
When dealing with unsupervised satellite images classification task, an algorithm such as K-means or ISODATA is chosen to take a data set and find a pre-specified number of statistical clusters in a multispectral space. These standard methods are limited because they require a priori knowledge of a probable number of classes. Furthermore, they also use random principles which are often locally optimal. Several approaches can be used to overcome these problems. In this paper, we are interested in approach inspired by the clustering of corpses and larval observed in real ant colonies. Based on previous works in this research field, we propose an ant-based multispectral image classifier. The main advantage of this approach is that it does not require any information on the input data, such as the number of classes, or an initial partition. Experimental results show the accuracy of obtained maps and so, the efficiency of developed algorithm.
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Khedam, R., Belhadj-Aissa, A. (2011). Classification of Multispectral Images Using an Artificial Ant-Based Algorithm. In: Cherifi, H., Zain, J.M., El-Qawasmeh, E. (eds) Digital Information and Communication Technology and Its Applications. DICTAP 2011. Communications in Computer and Information Science, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21984-9_22
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DOI: https://doi.org/10.1007/978-3-642-21984-9_22
Publisher Name: Springer, Berlin, Heidelberg
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