Application Of Rough Sets For Cluster To Remote Sensing Image

Remote Sensing image has attribution of complication, uncertainty and inadequate information. The uncertainty is a key issue for Remote Sensing theory and application, especially in classification. Evaluating and processing the uncertainty of the Remote Sensing information is an important task for RS application. As the general methods of Remote Sensing procession, spectrum and space analysis have limit because of essence of statistical method. So we choose Rough Sets for cluster that has developed in recent years to process Remote Sensing Image in this dissertation.Rough set theory is a new mathematic approach to uncertain and vague data analysis. It is, no doubt, one of the most challenging areas of modern computer applications nowadays and a new very important and rapidly growing area of research and applications. Rough set theory is applied to knowledge discovery, data reduction decision support, pattern recognition and others, and it has proved to be a very effective new mathematic approach. The theory found many, interesting real-life application in medicine, banking, engineering and others.This dissertation, firstly, introduces the scheme of rough set theory and its application. Then several improved research methods of processing of remote sensing image are put forward. A method is given to deal with image filter and enhancement based on rough set theory and this method can preserve the edge detail of image very well. Finally, rough set theory based cluster algorithms apply to the processing of remote sensing image. The detailed research work can be sum up as the following:1) According to analyses of rough set theory and the uncertainty characteristic in Remote Sensing information, the preponderance and feasibility are discussed in processing remote sensing image based on this theory.2) Regarded a remote sensing image as knowledge system, this paper proposes image filter and enhancement algorithms. These algorithms, based on rough set theory, can preserve the edge detail of image very well.3) Using equivalence relations of attributes of remote sensing image, rough set theory offers the number and the centroids of the clusters, which initialize the K-means clustering. And then the image is segmented by K-means clustering algorithm. Experiment results proved its validity and the method could be applied to remote sensing image processing.4) DBSCAN, a density based clustering algorithm, can efficiently discover clusters of arbitrary shape and effectively handle noise. However, it requires large volume ofmemory support and needs a lot of I/O costs on dealing with large-scale databases. So we put forward an improved method of DBSCAN based on rough set theory. The experimental results show that the new algorithm is adapted to processing of remote sensing image.