Improving Classification Accuracy of Hyperspectral Image Using Convolutional Neural Networks

Abstract

Hyperspectral image classification is a crucial aspect of remote sensing image analysis. Deep learning methods have been successfully used to classify remote sensing data. In recent years, convolutional neural networks (CNNs) have been significantly used in hyperspectral image classification, which has tried to overcome the computational and processing challenges of hyperspectral data. By increasing the number of parameters and layers of convolutional neural networks, their efficiency in solving complex problems decreases. For this reason, in this article, a new architecture of convolutional neural networks has been introduced, this network has a good performance and reduces the computing time.