A Study on Supervised Classification of Remote Sensing Satellite Image by Bayesian Algorithm Using Average Fuzzy Intracluster Distance
This paper proposes a more effective supervised classification algorithm of remote sensing satellite image that uses the average fuzzy intracluster distance within the Bayesian algorithm. The suggested algorithm establishes the initial cluster centers by selecting training samples from each category. It executes the extended fuzzy c-means which calculates the average fuzzy intracluster distance for each cluster. The membership value is updated by the average intracluster distance and all the pixels are classified. The average intracluster distance is the average value of the distance from each data to its corresponding cluster center, and is proportional to the size and density of the cluster. The Bayesian classification algorithm is performed after obtaining the prior probability calculated by using the information of average intracluster distance of each category. While the data from the interior of the average intracluster distance is classified by fuzzy algorithm, the data from the exterior of intracluster is classified by Bayesian classification algorithm. The testing of the proposed algorithm by applying it to the multispectral remote sensing satellite image resulted in showing more accurate classification than that of the conventional maximum likelihood classification algorithm.
KeywordsSatellite Image Supervise Classification Bayesian Algorithm Maximum Likelihood Classification Bayesian Classification
Unable to display preview. Download preview PDF.
- 1.Richards, J.A.: Remote Sensing Digital Image Analysis: An Introduction, 2nd, revised and enlarged edn., pp. 229–262. Springer, Heidelberg (1994)Google Scholar
- 3.Landgrebe, D.: Information Extraction Principles and Methods for Multispectral and Hyperspectral Image Data. In: Chen, C.H. (ed.) Information Processing for Remote Sensing, ch. 1, pp. 1–30. World Scientific Publishing Co., Inc., Singapore (1999)Google Scholar
- 4.Saglam, M.I., Yazgan, B., Ersoy, O.K.: Classification of Satellite Images by using Self-organizing map and Linear Support Vector Machine Decision tree. In: GIS development Conference Proceedings of Map Asia (2003)Google Scholar
- 5.Wu, Z.: Research on remote sensing image classification using neural network based on rough sets. In: 2001 International Conferences on Info-tech and Info-net ICII 2001-Beijing, Proceedings, vol. 1, pp. 279–28429 (2001)Google Scholar
- 10.Perera, A.S., Serazi, M.H., Perrizo, W.: Performance Improvement for Bayesian Classification on Spatial Data with P-Trees. In: 15th International Conference on Computer Applications in Industry and Engineering (2002)Google Scholar
- 11.Liang, Q.: MPEG VBR video traffic classification using Bayesian and nearest neighbor classifiers. In: IEEE International Symposium on Circuits and Systems ISCAS, pp. II-77-II-80 (2002)Google Scholar
- 12.Gustafson, D., Kessel, W.: Fuzzy clustering with a fuzzy covariance matrix. In: Proc. IEEE CDC, San Diego, USA, pp. 761–766 (1979)Google Scholar