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Clustering of Hyperspectral Image Data by Constructing a Diffuse Map

One of the problems of image processing and machine learning problems is the possibility of separating the classes. In our case, the proposed map is constructed in which the spectra of different surfaces areas associated with different materials, is clearly divided. In this approach, we rely on the method of "diffuse map". The basic idea is that the multidimensional data are projected into the mathematical variety of small dimension while preserving the mutual relations between the data. We describe a diffuse process that reveals hidden patterns that exist between the spectra of separating the different layers of the background. The model is based on a random walk on a graph. A walk on the graph is in the areas of condensation density. Random walk divides the region into separate clusters, which are caused by hidden relationships between elements of the set. As a result, we have used the technique of stochastic processes in the Markov chains for the separation of the clusters and identify relationships between data.