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This paper presents an unsupervised learning method based on Kohonen’s SOFM for clustering multispectral data. Neptune image has been considered for this purpose. Results have been very encouraging.
(T.N. Nagabhushana and D.S.Vinod, Classification of Remotely Sensed Data using Self
Organizing Feature Map, International Conference on Remote sensing...
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... experiment is conducted on multispectral Voyager 2 data covering Great Dark spot of planet Neptune (as shown in figure 1). The image was shuttered at a distance of 2.8 million kilometers. The original image of 700 samples X 852 lines is condensed to 70 samples X 86 lines With 3 features. The image is about feathery white clouds that overlie the boundary of dark and light blue regions. The classification map shows the spot classified into 7 clusters as shown in figure 2 and ...
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Citations
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.
(D.S. Vinod and T.N. Nagabhushana, Classification of Remotely Sensed Data using
Gravitational Symbolic Clustering, International Conference Map India 2003 held in
New Delhi, India on 28th to 31st of January 2003. Organized and published by GIS Development, India. )
Multispectral image data are well adopted for monitoring and analyzing the behavior of different covers and also the temporal changes occurring on the target. Periodically collecting the data and then comparing it with the previous data can observe this. 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 multispectral data 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. Here the multispectral data is used to classify, Satellite and Biomedical images. We propose an improvised method of classification of such data set using gravitational symbolic clustering. Symbolic data is a special case of conventional data, which is closer to real life interpretation and analysis. 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 employed. The data reduction technique uses bin arrays to store useful information of the reduced image. The major idea is to present a clustering algorithm based on gravitational approach. The procedure is based on the physical phenomenon in which a system of particles in space converge to the centroid of the system due to the gravitational attraction between the particles. 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 some time when no more mutual pairs are available for merging. A detailed study is carried on different sets of multispectral images and results are presented.
(D.S. Vinod and T.N. Nagabhushana, Classification of Multispectral Images using Symbolic Gravitational Approach, National Conference in Recent Trends in Information Technology, page 29 to 34, held at Karpagam Arts and Science College, Coimbatore, India on 22nd to 24th of August 2002. Organized and published by Department of Computer Science, Karpagam Arts and Science College, Coimbatore, India.)
Multispectral image data are well adopted for monitoring and analyzing the behavior of different covers and also the temporal changes occurring on the target. Periodically collecting the data and then comparing it with the previous data can observe this. 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 multispectral data 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. Here the multispectral data is used to classify, Satellite and Biomedical images. We propose an improvised method of classification of such data set using gravitational symbolic clustering. Symbolic data is a special case of conventional data, which is closer to real life interpretation and analysis. 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 employed. The data reduction technique uses bin arrays to store useful information of the reduced image. The major idea is to present a clustering algorithm based on gravitational approach. The procedure is based on the physical phenomenon in which a system of particles in space converge to the centroid of the system due to the gravitational attraction between the particles. 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 some time when no more mutual pairs are available for merging. A detailed study is carried on different sets of multispectral images and results are presented.
