Deep Learning for Hyperspectral Imagery Classification: The State of the Art and Prospects
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摘要: 高光谱图像（Hyperspectral imagery，HSI）分类是高光谱遥感对地观测技术的一项重要内容，在军事及民用领域都有着重要的应用.然而，高光谱图像的高维特性、波段间高度相关性、光谱混合等使得高光谱图像分类面临巨大挑战.近年来，随着深度学习新技术的出现，基于深度学习的高光谱图像分类在方法和性能上得到了突破性的进展，为其研究提供了新的契机.本文首先介绍了高光谱图像分类的背景、研究现状及几个常用的数据集，并简要概述了几种典型的深度学习模型，最后详细介绍了当前的一些基于深度学习的高光谱图像分类方法，总结了深度学习在高光谱图像分类领域中的主要作用和存在的问题，并对未来的研究方向进行了展望.Abstract: Hyperspectral imagery (HSI) classification occupies an important place in the earth observation technology of hyperspectral remote sensing, and it is widely used in both military and civil fields. However, due to HSI's characteristics including high dimensionality in data, high correlation between spectrum and mixing in spectrum, HSI classification faces great challenges. In recent years, as new deep learning technology emerges, the HSI classification methods based on deep learning have achieved some breakthroughs in methodology and performance and provided new opportunities for the research of HSI classification. In this paper, we review the research background, actuality of HSI classification technologies and several common datasets. Then, we provide a brief overview of several typical deep learning models. Finally, we introduce some deep learning based HSI classification methods in detail, summarize the main function and existing problems of deep learning in HSI classification, and present some prospects for future work.1) 本文责任编委 张军平
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图 7 基于谱特征和空间特征分别提取的分类流程图
Fig. 7 The flow chart of classification based on extracting spectral and spatial feature separately
图 8 基于3D-CNN同时提取谱特征和空间特征的分类流程图
Fig. 8 The flow chart of classification based on 3D-CNN extracting spectral and spatial feature simultaneously
表 1 高光谱图像分类常用数据集
Table 1 Several common datasets of HSI classification
数据 Indian Pines Salinas Kennedy Space Center Pavia Center Pavia University Botswana 采集时间 1992年 1992年 1996年 2001年 2001年 2001年 采集地点 印第安纳州 加利福尼亚州 佛罗里达 意大利北部 意大利北部 奥卡万戈三角洲 采集设备 AVIRIS AVIRIS AVIRIS ROSIS ROSIS Hyperion 光谱覆盖范围($\mu$m) 0.4 $\times$ 2.5 0.4 $\times$ 2.5 0.4 $\times$ 2.5 0.43 $\times$ 0.86 0.43 $\times$ 0.86 0.4 $\times$ 2.5 数据大小(像素) 145 $\times$ 145 512 $\times$ 217 512 $\times$ 614 1 096 $\times$ 492 610 $\times$ 340 1 476 $\times$ 256 空间分辨率(m) 20 3.7 18 1.3 1.3 30 波段数 224 224 224 115 115 242 去噪后波段数 200 204 176 102 103 145 样本量 10 249 54 129 5 211 7 456 42 776 3 248 类别数 16 16 13 9 9 14 
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表 2 几种主流的深度学习开发工具
Table 2 Several mainstream development tools of deep learning
工具 机构 支持语言 官网 Tensorflow Google Python/C++/Go/Java https://www.tensorflow.org/ Theano U Montreal Python http://deeplearning.net/software/theano/ Pytorch Facebook Python http://pytorch.org/ Caffe BVLC C++/Python/Matlab http://caffe.berkeleyvision.org/ CNTK Microsoft C++/Python/C http://cntk.codeplex.com/ Matconvnet / Matlab http://www.vlfeat.org/matconvnet/ MXNet DMLC Python/C++/R/Julia/Scala/Go/Matlab/JavaScript http://mxnet.io/index.html Torch Facebook Lua http://torch.ch/ Deeplearning4J DeepLearning4J Java/Scala https://deeplearning4j.org/
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