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

Abstract

GIS can manage remotely sensed images, users must have an appropriate digital map that represents lands each one has information according to 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.