Nowadays remote sensing image classification process has been most commonly used for object identification. It identifies the object in the remote sensing images by assigning the land cover classes to pixels. In this paper, a review on conventional and advanced remote sensing image classification techniques such as supervised, unsupervised, per pixel, sub pixel and object based image analysis processes has been provided. Further, a brief description about the effective features of different image classification algorithms like Fuzzy classifier, classification based on Artificial Neural Network (ANN), classification based on Support Vector Machine (SVM), Evolutionary Algorithms (EA) and Optimum Path Forest classification algorithms were also given. In the next section of paper various classification methodologies with their characteristics and examples of classifiers are explained. Moreover, this study compares the frequently used image classification algorithms and suggests the remote sensing image classifier to choose the best image classification technique based on the performance of classification that improves the accuracy range.

Remote sensing, Image classification, ANN, SVM, Optimum Path Forest

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