Support vector machine versus convolutional neural network for hyperspectral image classification: A systematic review
Ajay Kaul, Sneha Raina
Abstract
Abstract Various machine learning and deep learning techniques have been proposed for classification purposes in the case of hyperspectral imaging. Among all the machine learning techniques support vector machine (SVM) has been a promising classification algorithm in the case of remote sensing applications, particularly in the field of hyperspectral imaging. In the case of deep learning techniques, the convolutional neural network is gaining much attention from researchers for classification purposes and has given reliable results in this field, thus is being used widely by researchers. So, in this article, a systematic review about the two most used hyperspectral classification techniques that is, SVM and CNN is given. A total of 86 papers from the four well‐known journals belonging to the field of remote sensing have been reviewed where the methodologies used by different authors, datasets used, results acquired, contribution and shortcomings have been put forth. This meta‐analysis generally focuses on the recent SVM and CNN methodologies that are proposed by various authors. A summary of the best results obtained from SVM and CNN in terms of classification accuracies is also provided to help the researchers working in this area achieve better results.