The Key Technology Of Fuzzy Cluster Analysis Of Remote Sensing Images Based On Triple I Algorithm

With the development of remote sensing technology, the image information is enriched constantly, which makes the application of remote sensing image has been rapid promotion. Especially in image clustering process, the different types of color information has been showed better by high-resolution remote sensing image characteristics. In the surface features extraction, mapping, soil erosion testing, forest classification, land covering and other practical applications fully demonstrated its superiority. Thus clustering analysis of remote sensing image has broad application prospects.In the current supervised clustering algorithm, the results of Bayes algorithm for clustering was considered more better generally, but the results still cannot reach a satisfactory level. The main problems are: (1) Traditional Bayes algorithm has a strong subjective. With the increase in the number of image categories, the error of the category prior probability obtained by subjective observing also been increased, so it needs for new ways to solve the problem of subjectivity; (2) With the improvement of image resolution, the information of image will be more detailed, and it is also more comprehensive. So the traditional algorithms,which through simple sum and then taking the average of the feature extraction, are difficult to overcome the impact of the presence of noise point or mixed pixels in the sample area, therefore it needs new feature extraction algorithm to corresponding; (3) Image?clustering can be viewed as a pattern recognition problem, but the same pattern's color maybe not the same or different patterns have the same part, which increasing the difficulty of clustering.?How to cluster for remote sensing image, fully integrated into some new theoretical knowledge and methods are essential.This paper tightly around to improve the clustering accuracy of the remote sensing images as a main line, the use of low-altitude remote sensing image as the main data, focusing on discussing the two aspects: the impact factors of the traditional Bayes algorithm and constructing a new clustering algorithm. On the basis of existing research, proposed a new clustering algorithm for remote sensing images and sums up the scale to determine the best method of clustering.To solve the above problems, the main research and results of this paper are as follows:(1) Based on the study of the traditional Bayes clustering algorithm, modifying the traditional subjective prior probability by constructing fuzzy membership function to improve the accuracy of remote sensing image of clustering. (2) In the traditional algorithms on feature extraction, the feature extraction of each category is based on the same weight of the sample element. By studying the theory of gray relation, and combined with the color characteristic of high-resolution remote sensing image, an algorithm on feature extraction of image is proposed which can overcome the mixed pixel or noise points influence better.?(3) By using variety of information in the region on fuzzy surveillance cluster algorithm, studied the triple I algorithm of fuzzy theory to take cluster analysis on remote sensing images. In conclusion, how to improve the clustering accuracy is a key point in clustering analysis of remote sensing images. This article has done a series of studies focuses on the aspects, experiments show that the proposed algorithm in this article is better to improve the accuracy of clustering on remote sensing images.