ABSTRACT

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.

摘要

近来，包括卷积神经网络（CNN），递归神经网络（RNN）和递归卷积神经网络（R-CNN）的深度学习（DL）技术已被广泛用于对遥感数据进行分类。在各种深度学习算法中，基于CNN的算法最广泛用于卫星图像分类。尽管CNN的性能有所提高，但它还需要各种超参数来训练网络体系结构以实现所需的分类精度。考虑到任何分类算法实现的精度都会受到超参数的选择和取值的影响，本文使用三个遥感数据集，讨论了几种超参数对CNN分类器分类精度的影响。这项研究的目的不是提出一组不同的超参数值，而是使用遥感数据集研究它们对土地覆盖分类精度的影响。该研究的实验结果表明，各种超参数在不同程度上影响CNN分类器的性能，这表明需要使用考虑的数据集为土地覆盖分类研究选择这些超参数的最佳值。