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《高光谱遥感智能处理》
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作者：张良培，钟燕飞，王心宇
本书结合作者及所在课题组多年从事高光谱遥感研究内容，全面梳理高光谱遥感观测-模型-应用体系，在全面介绍高光谱遥感的基本原理、星-空-地高光谱观测平台的基础上，系统阐述了高光谱遥感影像去噪、混合像元分解、像素分类和目标探测的前沿技术，并呈现了高光谱信息技术在传统自然资源监测、地质调查和前沿的深空探测、工业视觉和公共安全等领域的应用案例，内容丰富详实且创新前沿。
本书是一部反映前沿高光谱遥感智能信息处理和行业应用的研究著作，可供从事高光谱遥感技术、地球科学、模式识别等领域的科研人员、专业技术人员，并可作为相关专业师生的学习参考书。
为了方便读者进一步学习、了解高光谱遥感智能处理，我们在此搜集整理了一些已经开源、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：wangxinyu@whu.edu.cn）
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高光谱去噪 |
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数据
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1. EO-1 Hyperion
Download
ZHONG Y, LI W, WANG X, et al. Satellite-ground integrated destriping network: A new perspective for EO-1 Hyperion and Chinese hyperspectral satellite datasets [J]. Remote Sensing of Environment, 2020, 237: 111416.
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2. HJ-1A
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ZHONG Y, LI W, WANG X, et al. Satellite-ground integrated destriping network: A new perspective for EO-1 Hyperion and Chinese hyperspectral satellite datasets [J]. Remote Sensing of Environment, 2020, 237: 111416.
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3. SPARK
Download
ZHONG Y, LI W, WANG X, et al. Satellite-ground integrated destriping network: A new perspective for EO-1 Hyperion and Chinese hyperspectral satellite datasets [J]. Remote Sensing of Environment, 2020, 237: 111416.
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4. CRISM
Download
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5. CAVE
Download
YASUMA F, MITSUNAGA T, ISO D, et al. Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum [J]. IEEE transactions on image processing, 2010, 19(9): 2241-53.
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6. ICVL
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ARAD B, BEN-SHAHAR O. Sparse recovery of hyperspectral signal from natural RGB images; proceedings of the Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016 [C].Springer, 2016.
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方法
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1. LRMR
Code
ZHANG H, HE W, ZHANG L, et al. Hyperspectral Image Restoration Using Low-Rank Matrix Recovery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 4729-43.
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2. LLRSSTV
Code
HE W, ZHANG H, SHEN H, et al. Hyperspectral Image Denoising Using Local Low-Rank Matrix Recovery and Global Spatial–Spectral Total Variation [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(3): 713-29.
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4. LRTF_DFR
Code
ZHENG Y B, HUANG T Z, ZHAO X L, et al. Double-Factor-Regularized Low-Rank Tensor Factorization for Mixed Noise Removal in Hyperspectral Image [J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(12): 8450-64.
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5. SGIDN
Code
ZHONG Y, LI W, WANG X, et al. Satellite-ground integrated destriping network: A new perspective for EO-1 Hyperion and Chinese hyperspectral satellite datasets [J]. Remote Sensing of Environment, 2020, 237: 111416.
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高光谱解混 |
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数据
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1. CA-Cropland
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WANG X, ZHONG Y, CUI C, et al. Autonomous endmember detection via an abundance anomaly guided saliency prior for hyperspectral imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(3): 2336-51.
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2. Urban
Download
KALMAN L S, BASSETT III E M. Classification and material identification in an urban environment using HYDICE hyperspectral data; proceedings of the Imaging Spectrometry III, 1997 [C].SPIE.
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3. Jasper Ridge
Download
RODARMEL C, SHAN J. Principal component analysis for hyperspectral image classification [J]. Surveying and Land Information Science, 2002, 62(2): 115-22.
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4. Cuprite
Download
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5. RMMS
Download
CUI C, ZHONG Y, WANG X, et al. Realistic Mixing Miniature Scene Hyperspectral Unmixing: from Benchmark Datasets to Autonomous Unmixing [J]. IEEE Transactions on Geoscience and Remote Sensing, 2023.
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方法
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1. SAED
Code
WANG X, ZHONG Y, CUI C, et al. Autonomous endmember detection via an abundance anomaly guided saliency prior for hyperspectral imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(3): 2336-51.
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2. SGSNMF
Code
WANG X, ZHONG Y, ZHANG L, et al. Spatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing [J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(11): 6287-304.
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3. MVSA
Code
LI J, AGATHOS A, ZAHARIE D, et al. Minimum volume simplex analysis: A fast algorithm for linear hyperspectral unmixing [J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(9): 5067-82.
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4. CyCU-Net
Code
GAO L, HAN Z, HONG D, et al. CyCU-Net: Cycle-consistency unmixing network by learning cascaded autoencoders [J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-14.
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5. GBM
Code
HALIMI A, ALTMANN Y, DOBIGEON N, et al. Nonlinear unmixing of hyperspectral images using a generalized bilinear model [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4153-62.
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6. PPNMM
Code
ALTMANN Y, HALIMI A, DOBIGEON N, et al. Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery; proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), F, 2011 [C]. IEEE.
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7. MVCNMF
Code
MIAO L, QI H. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization [J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(3): 765-77.
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8. UDAS
Code
QU Y, QI H. uDAS: An untied denoising autoencoder with sparsity for spectral unmixing [J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57(3): 1698-712.
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高光谱分类 |
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数据
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1. Botswana
Download
NEUENSCHWANDER A, CRAWFORD M, RINGROSE S. Results of the EO-1 experiment-Use of Earth Observing-1 Advanced Land Imager (ALI) data to assess the vegetational response to flooding in the Okavango Delta, Botswana [J]. International Journal of Remote Sensing, 2005.
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2. HyRANK
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KARANTZALOS K, KARAKIZI C, KANDYLAKIS Z, et al. HyRANK hyperspectral satellite dataset I (version v001) [J]. IW III/4, 2018.
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3. WHU-OHS
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LI J, HUANG X, TU L. WHU-OHS: A benchmark dataset for large-scale Hersepctral Image classification [J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 113: 103022.
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4. 雄安新区
Download
岑奕, 张立福, 张霞, 等. 雄安新区马蹄湾村航空高光谱遥感影像分类数据集 [J]. 遥感学报, 2020, 24(11): 1299-306.
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5. Washington DC
Download
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6. Pavia University & Center
Download
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7. Indian Pines
Download
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8. Salinas
Download
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9. DFC2013 Houston
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DEBES C, MERENTITIS A, HEREMANS R, et al. Hyperspectral and LiDAR data fusion: Outcome of the 2013 GRSS data fusion contest [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2405-18.
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10. DFC2018 Houston
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XU Y, DU B, ZHANG L, et al. Advanced multi-sensor optical remote sensing for urban land use and land cover classification: Outcome of the 2018 IEEE GRSS data fusion contest [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(6): 1709-24.
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11. KSC
Download
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12. Eagle_reize
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GHAMISI P, PHINN S. Fusion of lidar and hyperspectral data [M]//GHAMISI P, PHINN S. Figshare. 2015.
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13. BerlinUrbGrad 2009
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OKUJENI A, VAN DER LINDEN S, HOSTERT P. Berlin-urban-gradient dataset 2009-an enmap preparatory flight campaign [J]. 2016.
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14. Chikusei
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YOKOYA N, IWASAKI A. Airborne hyperspectral data over Chikusei [J]. Space Appl Lab, Univ Tokyo, Tokyo, Japan, Tech Rep SAL-2016-05-27, 2016, 5.
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15. AeroRIT
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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): 8116-24.
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16. WHU-Hi
Download
ZHONG Y, HU X, LUO C, et al. 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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17. UAV-HSI-Crop
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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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方法
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1. FPGA
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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2. HybridSN
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): 277-81.
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3. 3-D CNN
Code
AHMAD M, KHAN A M, MAZZARA M, et al. A fast and compact 3-D CNN for hyperspectral image classification [J]. IEEE Geoscience Remote Sensing Letters, 2020, 19: 1-5.
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4. SSAN
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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5. PresNet
Code
PAOLETTI M E, HAUT J M, FERNANDEZ-BELTRAN R, et al. Deep pyramidal residual networks for spectral-spatial hyperspectral image classification [J]. IEEE Transactions on Geoscience Remote Sensing, 2018, 57(2): 740-54.
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6. SSRN
Code
ZHONG Z, LI J, LUO Z, et al. Spectral-spatial residual network for hyperspectral image classification: A 3-D deep learning framework [J]. IEEE Transactions on Geoscience Remote Sensing, 2017, 56(2): 847-58.
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高光谱异常检测 |
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数据
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1. ABU
Download
KANG X, ZHANG X, LI S, et al. Hyperspectral anomaly detection with attribute and edge-preserving filters [J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(10): 5600-11.
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2. AVIRIS-1
Download
WANG S, WANG X, ZHONG Y, et al. 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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3. HYDICE
Download
BASEDOW R W, CARMER D C, ANDERSON M E. HYDICE system: Implementation and performance; proceedings of the Imaging Spectrometry, 1995 [C].SPIE, 1995.
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4. WHU-Hi-River
Download
WANG S, WANG X, ZHONG Y, et al. 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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方法
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1. RXD
Code
REED I S, YU X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution [J]. IEEE Transactions on Acoustics Speech & Signal Processing, 1990, 38(10): 1760-70.
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2. LRASR
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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3. ADLR
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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4. Auto-AD
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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5. CRD
Code
WEI L, QIAN D. Collaborative Representation for Hyperspectral Anomaly Detection [J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3): 1463-74.
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6. RSAD
Code
DU B, ZHANG L. Random-Selection-Based Anomaly Detector for Hyperspectral Imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(5): 1578-89.
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