Satellite Image Classification Using Extended Local Binary Patterns, SVM AND CNN

Abstract

In many disciplines and applications, image processing has shown to be an effective research tool. Numerous uses, such as disaster response, law enforcement, and environmental monitoring, depend on satellite imaging. Applications demand manual object and device identification from photos. ELBP-SVM technique is used to categorize the satellite images into a set of distinct classes. Although this work is able to classify distinct classes in addition to the class of satellite images, identifying the characteristics of these other classes, such as forest, desert, oceans, etc., is also straightforward because these other classes have some unique characteristics that can be easy to distinguish and therefore easy to classify. This work uses the suggested ELBP approach to first find local binary patterns. The SVM classifies the test picture class after acquiring the extended features. The ELBP-SVM approach is utilized in this study, and the percentage of correctly identified satellite images is 97%. The results discovered and experimentally acquired on MATLAB 2020a are superior to other research currently accessible for the classification of satellite photos.