Novel Optimization Technique for Classification of Remote Sensing Data Using Svm

Abstract

Remote sensing data is a collection of images and interpretation of information about an object, area, or event without any physical contact with it. Aircraft and satellites are common remote sensing platforms for earth and its natural sources. Remote sensing’s ability to identify and monitor land surfaces and environmental conditions expanded over years with remote sensed data being essential in natural resource management. Machine learning is used for classification of the remote sensed images. This study uses Support Vector Machine (SVM) with Radial Basis Function (RBF) for classifying Remote Sensing (RS) images. RBF kernel improves classification accuracy. This study proposes SVM-RBF optimization with Cuckoo Search (CS) for remote sensing data classification.

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