Deep Learning for Hyperspectral Image Classification: An Overview
Deep Learning for Hyperspectral Image Classification: An Overview
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DOI：
10.1109/tgrs.2019.2907932
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发表时间：
2019-09-01
影响因子：
8.2
通讯作者：
Benediktsson, Jon Atli
中科院分区：
文献类型：
--
作者：
Li, Shutao;Song, Weiwei;Benediktsson, Jon Atli
高光谱图像分类已成为遥感领域的研究热点。通常，高光谱数据的复杂特性使得对这类数据的准确分类对传统的机器学习方法构成了挑战。此外，高光谱成像通常处理被捕获的光谱信息与相应材料之间的内在非线性关系。近年来，深度学习被公认为是一种有效解决非线性问题的强有力的特征提取工具，并被广泛应用于许多图像处理任务中。在这些成功应用的激励下，深度学习也被引入到HSI分类中，并表现出了良好的性能。本文系统地回顾了基于深度学习的HSI分类文献，并比较了几种针对这一主题的策略。具体地说，我们首先总结了传统机器学习方法无法有效克服的HSI分类的主要挑战，并介绍了深度学习在处理这些问题方面的优势。然后，我们构建了一个框架，将相应的工作划分为光谱特征网络、空间特征网络和光谱空间特征网络，系统地回顾了基于深度学习的HSI分类的最新成果。此外，考虑到遥感领域可用的训练样本通常非常有限，而训练深度网络需要大量的样本，我们提出了一些改进分类性能的策略，这可以为未来对这一主题的研究提供一些指导。最后，在实验中对几种具有代表性的基于深度学习的分类方法进行了实验。
Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework that divides the corresponding works into spectral-feature networks, spatial-feature networks, and spectral-spatial-feature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments.