Analysis of Hyperspectral Image Classification Technology and Application Based on Convolutional Neural Networks

Abstract

In recent years, with the rapid development of computer software and hardware, it is becoming a growing number of popular using deep learning to process images. Therefore, many scholars also focus on the field of hyperspectral image (HSI) classification of deep neural networks. This article mainly introduces deep neural networks in HSI processing, including stacked auto-encoders, deep belief networks, and convolutional neural networks (CNNs). At the same time, due to the significant advantages of CNNs for HSI processing, this article also mainly summarizes the methods that scholars have used CNN for image classification over the year. Meanwhile, various classification networks related to the CNN architecture are summarized. After that, this article compares the advantages, disadvantages, and characteristics of different networks. Finally, combined with the existing problems, some future directions are proposed for HSI classification.