Application of artificial neural networks for classifying surface areas with a certain relief

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Application of artificial neural networks for classifying surface areas with a certain relief

Journal Title: Геодезія, картографія і аерофотознімання - Year 2016, Vol 83, Issue 83

Abstract

The purpose of research. The main purpose of research is to analyze the relief of various surfaces. For example, to select on the surface the individual sections of a certain form, such as slopes that are oriented in a given direction. The main aim of the article is the use of artificial neural networks (ANN). To solve the problem of classification a binary classifier was created and its work and its accuracy was studied. Method. The research was carried out on the certain section of the earth's surface. The digital model, presented by greed file, was created. The heights at the intersections of grid squares, or matrix 2117 were determined. From this matrix the images, that is separate windows of the surface areas measuring 3x3 intersection were made. Even image was made as a vector, that is the slopes from the central point of the window at the other 8 border points. The surface relief was presented by 77 images. The next step was to create a binary classifier. It divides objects (land surface) with the slopes from west to east into one group, and the rest – into the second. For this goal Module data processing algorithms based on artificial neural networks in MATLAB Software Package was used. It selected input, hidden and output neuron layers and conducted its study, performed the simulation and the testing. Classification process was carried on the base of ANN. Input data were presented by matrix size images 877. The matrix of targets had 277 dimensions. Its elements hada value of 0 or 1, depending on the class to wich the site belong. The third matrix (test) had 8x8 dimension. Classification and assessment of its accuracy was performed in two ways using the graphical editor nntool and nprtool. Results. The work of the created classifier was checked using the test images. The test, used in the studies was a matrix consisting of eight columns. Two columns of this matrix were images of slopes oriented from west to east – one close to them, and the rest – images of freeform surfaces. Assessment of the classifier was performed using confusion matrix. The total number of correctly classified samples was about 99 percent. Scientific novelty and practical value. Experimental research on the selection of surface areas with slopes of a certain orientation and analysis of the results give reasons for their use in different studies. It can be microrelief of the mechanical parts, various biological objects and, of course, the earth relief, which largely determines the fertility of agricultural lands, affects the ecological hazards such as: floods, mudflows, landslides and snow avalanche. So, development and improvement objective methods for classification surface areas is an urgent task.

Authors and Affiliations

Roman Rudyi

Keywords

classification topography orientation classification accuracy artificial neural networks

EP ID EP193727
DOI -
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