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Earth surface image analysis sensitivity improvement by means of combination of fuzzy and neuralnet segmentation methods

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Category: Information technologies, systems analysis and administration
Last Updated on 25 December 2013
Published on 19 December 2012
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 Authors:

I.M. Udovik, State Higher Educational Institution «National Mining University», Assistant Lecturer of the Department of Computer Systems Software, Dnipropetrovsk, Ukraine

Abstract:

Purpose. To increase sensitivity of visual analysis of low-contrast images of Earth surface and geophysical fields.

Methodology. We have increased the dimension of the space characteristics of the analyzed information in the image by using the method of fuzzy segmentation of initial data, and have formed new multi-dimensional array of images based on the obtained membership functions of classes with subsequent segmentation of the Kohonen neural network.

Findings. We have developed and experimentally proved the new method of segmentation of low-contrast images of the Earth surface and geophysical fields.

Originality. The new method of formation of multi-dimensional information space using images membership classes in the fuzzy segmentation algorithm followed by the formation of an adaptive single output image.

Practical value. The informativity of the method has been approved by experimental testing on segmentation of real examples of low-contrast images. The sensitivity of the procedure of segmentation, depending on the nature of the task may vary.

 

References: 
 
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Akhmetshina, L.G. and Egorov, A.A. (2002), “Segmentation of multispectral images using Kohonen self-organizing maps”, Collected scientific papers NGAU, Vol.2, no.14. pp. 154–158.
 
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