Abstract:

Hyperspectral Imagery（HSI） classification is an important task of hyperspectral image processing and application. With the development of deep learning, Convolutional Neural Network（CNN） has gradually become an effective solution to the classification problem of HSI. Firstly, the task of HSI classification is summarized, and the existing problems are analyzed. Secondly, CNN and its classification methods based on spectral features, spatial features and spatial-spectral features have been systematically sorted out, and the above classification methods are carried out experimental comparison. Finally, the key issues of HSI classification are summarized and future research directions are discussed.

Key words: hyperspectral image classification, deep learning, Convolutional Neural Network（CNN）, spatial-spectral features

摘要：

高光谱图像（Hyperspectral Imagery，HSI）分类是高光谱图像处理和应用的一项重要工作。随着深度学习的不断发展，卷积神经网络（Convolutional Neural Network，CNN）日渐成为处理高光谱遥感图像分类问题的一个有效方法。首先对高光谱遥感图像分类任务进行了概述，分析了目前存在的问题；其次对CNN及其基于光谱特征、空间特征、空谱特征的分类方法进行了系统的梳理，并且将上述的分类方法通过实验分析其性能；最后对高光谱遥感图像分类的关键问题进行了总结，并讨论了未来的研究方向。

关键词: 高光谱遥感图像分类, 深度学习, 卷积神经网络（CNN）, 空谱特征