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Abstract
Remotely sensed data is obtained through artificial satellites from various geographic and astronomical sources. Remotely sensed image data are well adopted for monitoring and analyzing the behavior of different covers and also the temporal changes occurring on the target. Observations are made by periodically collecting the data and then comparing previous data. Using various sensors, data is collected and further analyzed to obtain information about objects and areas under investigation. The number of features in each sample of the 'Remotely sensed data' or 'multispectral image' depends upon the number of channels from which the information is collected. Data from different channels are combined to obtain the detailed information about the body or area under observation. We propose an improvised method of classification of such data using gravitational symbolic clustering. Symbolic data is a special case of conventional data, which is closer to real life interpretation and analysis. In conventional data, the objects are individualized, whereas in symbolic data sets they are more unified by means of relationships. The multispectral image is a quantitative interval type of symbolic data. It requires lot of time and memory space to analyze or classify the data. In order to overcome such limitations a data reduction technique is proposed. The data reduction technique uses storage bin arrays to store useful information of the reduced image data. The major idea is to present a clustering algorithm based on gravitational symbolic approach. The procedure is based on the physical phenomenon in which a system of particles in space converges to the centroid of the system due to the gravitational attraction between the particles. Both Agglomerative and Disaggregative Gravitational Symbolic approaches are considered. The concept of mutual pairs is used to merge the samples. The process of merging reduces the number of samples each time they are available for consideration. The process terminates at a stage when no more mutual pairs are available for merging. A detailed study has been carried out on different sets of multispectral images. We have also compared our results using cluster indices.