Artificial Neural Network based classification using Textural Features for Remotely Sensed Data
*Miss. M.Shanmugapriya , **Miss.B.Sathya Bama , **Dr.Mrs.S.Raju
*Student, ** Faculty, Dept. of Electronics and Communication Engineering,
Thiagarajar College of Engineering, Madurai-625015.
E-mail: priya_shan85@yahoo.co.in,
sathyabama_in@yahoo.com,
srece@tce.edu
Abstract
Image Classification plays an important role in the fields of Remote sensing, Image analysis and Pattern Recognition. Digital image classification is the process of sorting all the pixels in an image into a finite number of individual classes. The conventional statistical approaches for land cover classification use only the gray values. However, they lead to misclassification due to strictly convex boundaries. . Texture is one of the most important properties of visual surface that helps in discriminating one object from another or an object from background. One of the main tasks of texture analysis is the recognition of image regions using their textural properties. Textural features can be included for better classification but are inconvenient for conventional methods. Artificial Neural Networks can handle non-convex decisions. The uses of textural features help to resolve misclassification. This paper describes the design and development of a Hierarchical Neural Network by incorporating textural features. The effect of inclusion of textual features on classification is also studied. This image classification is a two step process in which initial stage involves K means clustering technique followed by Expectation - Maximization algorithm. The second stage involves the computation of texture features using Gray level Co-Occurrence matrix followed by classification using Neural Networks. Thus this work aims to study Neural Networks used for the classification of natural texture images and suggest the Neural Network Architecture leading towards the goal of attaining high accuracy Image Classification.
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