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Abstract: To improve the accurate rate of mapping multi-spectral remote sensing images, in this paper we construct a class of HyperRectangular Composite Neural Networks (HRCNNs), integrating the paradigms of neural networks with the rule-based approach. The supervised decision-directed learning (SDDL) algorithm is also adopted to construct a two-layer network in a sequential manner by adding hidden nodes as needed. Thus, the classification knowledge embedded in the numerical weights of trained HRCNNs can be extracted and represented in the form of If-Then rules. The rules facilitate justification on the responses to increase accuracy of the classification. A sample of remote sensing image containing forest land, river, dam area, and built-up land is used to examine the proposed approach. The accurate recognition rate reaching over 99% demonstrates that the proposed approach is capable of dealing with image mapping. [Copyright &y& Elsevier]

Abstract: In this paper a new data projection algorithm which was inspired by the foraging behaviors of doves is proposed. We name the new data projection the swarm-inspired projection (SIP) algorithm. The algorithm allows us to visually estimate the number of clusters existing in a data set. Based on the projection result, we may then partition the data set into the corresponding number of clusters. The SIP algorithm regards each data pattern in a data set as a piece of crumb which will be sequentially tossed to a flock of doves on the ground. The doves will adjust their physical positions to compete for crumbs. Gradually, the flock of doves will be divided into several groups according to the distributions of the crumbs. The formed groups will naturally correspond to the underlying data structures in the data set. By viewing the scatter plot of the final positions of the doves we can estimate the number of clusters existing in the data set. Several data sets were used to demonstrate the effectiveness of the proposed SIP algorithm. [Copyright &y& Elsevier]