Parametric Study Of Convolutional Neural Network Based Remote Sensing Image Classification

Recently, deep learning (DL) techniques including Convolutional neural network (CNN), Recurrent neural network (RNN), and Recurrent-Convolutional neural network (R-CNN) have been extensively used to classify the remotely sensed data. Out of various deep learning algorithms, CNN-based algorithms are most widely used for the satellite image classification. Despite the improved performance of CNN, it also requires various hyper-parameters for training the network architecture to achieve the desired classification accuracy. Keeping in view the fact that the accuracy achieved by any classification algorithms is influenced by a suitable choice and value of hyper-parameter, this paper discusses the influence of several hyper-parameters on the classification accuracy of CNN classifier using three remote sensing datasets. The aim of this study is not to propose a set of values of different hyper-parameters but to study their influence on land cover classification accuracy with remote sensing datasets. Experimental results from the study indicate that various hyper-parameters affect the performance of CNN classifier to different extent suggesting a need to select the optimal value of these hyper-parameters for land cover classification studies using considered datasets.