ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 7, pp. 9-19

Hyperspectral image classification using cluster data structure

E.V. Ramenskaya 1 , M.P. Kuznetsov 2 , V.V. Ermakov 1 , O.R. Barkova 1 , A.A. Bran 1 
1 Samara State Technical University, Samara, Russia
2 Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia
Accepted: 20.11.2017
DOI: 10.21046/2070-7401-2017-14-7-9-19
The problem of two-class classification of a hyperspectral image is solved, with the subdivision into clusters within each class. The images were obtained during flight tests of a laboratory aircraft. At the first stage, the algorithm performs clustering of the areas of interest. The likelihood function for a mixture of Gaussian distributions is maximized. At the second stage, the algorithm uses the Mahalanobis distances to cluster centers as a feature space. In this space, the classification is carried out by the decision tree method which minimizes the classification errors. The algorithm has low computational complexity: the learning stage is linearly dependent on the number of marked pixels. The proposed method is interpreted within the framework of the expert field. The learning phase of the algorithm has linear computational complexity by the number of objects. The proposed method of classification makes it possible to identify with high accuracy the areas of interest in the hyperspectral image. The algorithm allowed to separate areas of oil contaminated soil from other poorly reflecting objects. The result of the algorithm is the image in which only areas with the presence of petroleum products are indicated by color, and all other areas are darkened. This image has the same resolution as the original image. Georeference is preserved. It is possible to count the marked pixels of the image in the received image to calculate the area of the contaminated sites.
Keywords: hyperspectral image, cluster hypothesis, mixture of distributions, Mahalanobis distance, decision tree
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