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高光谱高空间分辨率遥感观测、处理与应用
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作者: 钟燕飞，王心宇，胡鑫，王少宇，万瑜廷，唐舸，张良培
摘要:高光谱遥感技术是遥感领域的研究热点之一。然而，由于成像口径与能量等限制因素，难以同时获得高光谱和高空间分辨率的图像，这极大限制了高光谱遥感在精细尺度任务中的应用。近年来，随着高光谱成像技术及无人机为代表的新型观测平台的发展,高光谱高空间（双高，同时具备纳米级光谱分辨率与亚米级空间分辨率）遥感技术发展迅猛，推动了高光谱遥感技术的应用，但同时也带来了更多问题。极高的空间与光谱分辨率使得数据更加海量高维，加剧了高光谱数据的空间异质性和光谱变异性，为影像智能信息处理带来更大的挑战。为此，本文将从双高遥感影像基准数据集、双高遥感影像智能信息处理、双高遥感影像典型应用3个方面论述双高遥感应用与发展现状。
文章“高光谱高空间分辨率遥感观测、处理与应用”已经在测绘学报上发表.感兴趣的读者可以在测绘学报上下载全文阅读。并且为了方便读者进一步学习、了解双高遥感智能信息处理，我们在此搜集整理了综述中已经开源、共享的相关数据集和代码，并附上相应资源的原始链接。（涉及到资源共享的作者本人，如您对我们搜集整理的链接存有疑问或其他任何问题请与我联系邮箱：tangge@whu.edu.cn）
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双高遥感公开数据集 |
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● WHU-Hi (组内数据)
Download
Zhong Y, Hu X, Luo C, Wang X, Zhao J, Zhang L. WHU-Hi: UAV-borne hyperspectral with high
spatial resolution (H2) benchmark datasets and classifier for precise crop identification
based on deep convolutional neural network with CRF[J]. Remote Sensing of Environment, 2020,
250: 112012.
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● WHU-Hi-River (组内数据)
Download
Wang S, Wang X, Zhong Y, Zhang L. Hyperspectral anomaly detection via locally enhanced
low-rank prior[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(10):
6995-7009.
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● AeroRIT (公开数据)
Download
Rangnekar A, Mokashi N, Ientilucci E J, et al. Aerorit: A new scene for hyperspectral
image analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(11):
8116-8124.
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● 马蹄湾村数据集 (公开数据)
Download
雄安新区马蹄湾村航空高光谱遥感影像分类数据集
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● TAIGA (公开数据)
Download
Mõttus M, Pham P, Halme E, et al. TAIGA: A novel dataset for multitask learning of
continuous and categorical forest variables from hyperspectral imagery[J]. IEEE Transactions
on Geoscience and Remote Sensing, 2022, 60:
1-11.
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● Luojia-HSSR (公开数据)
Download
Xu Y, Gong J, Huang X, et al. Luojia-HSSR: A high spatial-spectral resolution remote
sensing dataset for land-cover classification with a new 3D-HRNet[J]. Geo-spatial
Information Science, 2022: 1-13.
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● UAV-HSI-Crop (公开数据)
Download
Niu B, Feng Q, Chen B, et al. HSI-TransUNet: A transformer based semantic segmentation
model for crop mapping from UAV hyperspectral imagery[J]. Computers and Electronics in
Agriculture, 2022, 201: 107297.
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双高遥感影像波段选择 |
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● ICA (公开方法)
Code
Du H, Qi H, Wang X, et al. Band selection using independent component analysis for
hyperspectral image processing[C]//32nd Applied Imagery Pattern Recognition Workshop, 2003.
Proceedings. IEEE, 2003: 93-98.
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● FNGBS (公开方法)
Code
Wang Q, Li Q, Li X. A fast neighborhood grouping method for hyperspectral band
selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(6): 5028-5039.
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● SpaBS (公开方法)
Code
Li S, Qi H. Sparse representation based band selection for hyperspectral images[C]//2011
18th IEEE International Conference on Image Processing. IEEE, 2011: 2693-2696.
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● ISSC (公开方法)
Code
Li S, Qi H. Sparse representation based band selection for hyperspectral images[C]//2011
18th IEEE International Conference on Image Processing. IEEE, 2011: 2693-2696.
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● BS-Net-FC (公开方法)
Code
Cai Y, Liu X, Cai Z. BS-Nets: An end-to-end framework for band selection of
hyperspectral image[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 58(3):
1969-1984.
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● BS-Net-Conv (公开方法)
Code
Cai Y, Liu X, Cai Z. BS-Nets: An end-to-end framework for band selection of
hyperspectral image[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 58(3):
1969-1984.
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双高遥感影像精细分类 |
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● FPGA (组内方法)
Code
Zheng Z, Zhong Y, Ma A, et al. FPGA: Fast patch-free global learning framework for fully
end-to-end hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote
Sensing, 2020, 58(8): 5612-5626.
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● SVM (公开方法)
Code
Chang C C, Lin C J. LIBSVM: a library for support vector machines[J]. ACM transactions
on intelligent systems and technology (TIST), 2011, 2(3): 1-27.
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● FNGBSA-FC-3DCNN (公开方法)
Code
Ahmad M, Khan A M, Mazzara M, et al. A fast and compact 3-D CNN for hyperspectral image
classification[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 19: 1-5.
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● HybridSN (公开方法)
Code
Roy S K, Krishna G, Dubey S R, et al. HybridSN: Exploring 3-D–2-D CNN feature hierarchy
for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2019,
17(2): 277-281.
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● SSAN (公开方法)
Code
Mei X, Pan E, Ma Y, et al. Spectral-spatial attention networks for hyperspectral image
classification[J]. Remote Sensing, 2019, 11(8): 963.
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● FullContNet (公开方法)
Code
Wang D, Du B, Zhang L. Fully contextual network for hyperspectral scene parsing[J]. IEEE
Transactions on Geoscience and Remote Sensing, 2021, 60: 1-16.
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● SSDGL (公开方法)
Code
Zhu Q, Deng W, Zheng Z, et al. A spectral-spatial-dependent global learning framework
for insufficient and imbalanced hyperspectral image classification[J]. IEEE Transactions on
Cybernetics, 2021, 52(11): 11709-11723.
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双高遥感影像目标探测 |
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● Auto-AD (组内方法)
Code
Wang S, Wang X, Zhang L, et al. Auto-AD: Autonomous hyperspectral anomaly detection
network based on fully convolutional autoencoder[J]. IEEE Transactions on Geoscience and
Remote Sensing, 2021, 60: 1-14.
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● CEM (公开方法)
Code
Harsanyi J C. Detection and classification of subpixel spectral signatures in
hyperspectral image sequences[M]. University of Maryland, Baltimore County, 1993.
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● ACE (公开方法)
Code
Kraut S, Scharf L L. The CFAR adaptive subspace detector is a scale-invariant GLRT[J].
IEEE Transactions on Signal Processing, 1999, 47(9): 2538-2541.
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● STD (公开方法)
Code
Chen Y, Nasrabadi N M, Tran T D. Sparse representation for target detection in
hyperspectral imagery[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(3):
629-640.
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● LRASR (公开方法)
Code
Xu Y, Wu Z, Li J, et al. Anomaly detection in hyperspectral images based on low-rank and
sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 54(4):
1990-2000.
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● ADLR (公开方法)
Code
Qu Y, Wang W, Guo R, et al. Hyperspectral anomaly detection through spectral unmixing
and dictionary-based low-rank decomposition[J]. IEEE Transactions on Geoscience and Remote
Sensing, 2018, 56(8): 4391-4405.
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