Crop Type Classification Based on Clonal Selection Algorithm for High Resolution Satellite Image

This paper presents a hierarchical clustering algorithm for crop type classification problem using multi-spectral satellite image. In unsupervised techniques, the automatic generation of clusters and its centers is not exploited to their full potential. Hence, a hierarchical clustering algorithm is proposed which uses splitting and merging techniques. Initially, the splitting method is used to search for the best possible number of clusters and its centers using non-parametric technique i.e., clonal selection method. Using these clusters, a merging method is used to group the data points based on a parametric method (K-means algorithm). The performance of the proposed hierarchical clustering algorithm is compared with two unsupervised algorithms (K-means and SelfOrganizing Map) that are available in the literature. A performance comparison of the proposed algorithm with the conventional algorithms is presented. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is more accurate.