Satellite Image Classification Method Using ELBP and SVM Classifier

A machine learning-based Support vector machine (SVM) and Extended Local Binary Patterns (ELBP) methods have been used for the classification of satellite images among a set of 24 different classes. This work does not only classify the satellite image class this work is also capable of classifying 24 different class however to identify the features of those other classes like the human face, football, dog, etc. is also simple because these other classes have some exclusive features which can be simply to differentiate hence easy classification. In the case of the satellite image, the major issue is that different satellite images may have different features that make satellite image classification hard. Another issue is that safelight images are normally noise corrupted. The noise patterns of the wireless image are estimated using SVM Classifier, and with those estimated noise patterns removed by SVM algorithm of signal classification. This work first performs segmentation on the input test image to be classified then finds out the local binary patterns using the proposed ELBP method. The Extended LBP is needed because to differentiate the patterns of different satellite images and different other class images cannot be estimated with LBP only. Once extended features obtained SVM classifies the class of the test image. The method used in this work is ELBP-SVM and the Satellite image correct recognition obtain is 94%. The experimental results obtained on MATLAB 2018b and the results found are better than other available work t. classify satellite images.