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Analysis of GLCM Feature Extraction for Choosing Appropriate Angle Relative to BP Classifier

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 توفيق عبد الخالق عباس الاسدي 17/06/2014 07:12:54
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Analysis of GLCM Feature Extraction for Choosing Appropriate Angle Relative to BP Classifier

Dr. Tawfiq A. Alasadi1 , Wadhah R. Baiee2

1(Dean of Information Technology College/ Babylon University, Iraq)
2(Software Department, Information Technology College/ Babylon University, Iraq)


Abstract:-

GIS can manage remto its owner, status, and some other data. The classification of such lands is a great problem which take long time depending on human efforts. Many kinds of classifications had been used , one of them is the use of supervised multi-layer perceptron with backpropagation neural network classifier and using second order statistics Gray Level Co-occurrence Matrix (GLCM) to calculate eight textural features for each one of three visible bands (RGB) for each land sample. In this research we analyzed the GLCM feature extraction algorithm to detect the appropriate angle that can be chosen , relatively with the training of BP classifier had been used according to the number of hidden nodes inside the hidden layer of ANN . As a result the system produce high accuracy with the best angle choosing of GLCM , these results are achieved by comparing the classification results from system test trials with desired user predefined classification dataset.

Keywords: classification, features extraction, GIS, GLCM, neural networks, texture.

I. INTRODUCTION:-

Geographic Information System (GIS) are commonly defined as an information system that manages, manipulates, and analyzes spatial data [1] .Geographical-Spatial data has both spatial and thematic components. GIS have to be able to manage both elements. Spatial component, the observations have two aspects in its localization, absolute localization based in a coordinates system and topological relationship referred [2]. The classification aim is to appoint each object in the study area to one or more elements of a defined label set, so that the radiometric information contained in the image is converted to thematic information, The process can be regarded as a mapping function that constructs a linkage between the raw data and the user-defined label set [3]. There are two types of classification ;supervised classification methods which are based on prior knowledge of certain aspects of the statistical nature of the spectral classes of an image pixels are to be identified, and unsupervised classification methods which are performed by using a classification algorithm without any predefinition of spectral classes of interest [4][5]. Texture is an appropriate property of objects. It contains important information about the structural arrangement of surfaces. The use of texture in addition to spectral features for image classification might be expected to result in some level of accuracy improvement, depending on the spatial resolution and the size of the area being classified [6].



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