Clustering Algorithm for Texture Data from Remote Sensing
A histogram-based clustering algorithm is proposed that takes into account features of the collection of image texture statistics. The algorithm allows the addition of the false clusters occurring on the boundaries of objects with different textures, thus significantly reducing their number. The clusters are analyzed by estimating their separability in the multidimensional vector space of features and the image context. The application of the algorithm to the automated recognition of types of land cover from aerial photographs of forest landscapes is considered. A comparison of cluster maps and schematic map of ground survey shows their good agreement.