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Multimodal hyperspectral remote sensing: an overview and perspective

Yanfeng Gu, Tianzhu Liu, Guoming Gao, Guangbo Ren, Yi Ma, Jocelyn Chanussot, Xiuping Jia
2021 Science China Information Sciences  
, and potential challenges in data representation, feature learning and interpretation.  ...  Through the analysis of development trend of hyperspectral imaging and current research situation, we hope to provide a direction for future research on multimodal hyperspectral remote sensing.  ...  the common subspace from hyperspectral-multispectral correspondences [137] .  ... 
doi:10.1007/s11432-020-3084-1 fatcat:tivcc4l5efh5zg62t37stswqgu

Table of contents

2021 IEEE Transactions on Geoscience and Remote Sensing  
Vegetation and Land Surface Statistical Scattering Component-Based Subspace Alignment for Unsupervised Cross-Domain PolSAR Image Classification .........................................................  ...  NET: Spatial-Spectral Reconstruction Network for Hyperspectral and Multispectral Image Fusion ............... ...................................................................................... X.  ... 
doi:10.1109/tgrs.2021.3083444 fatcat:vlhsfoh76zf6zp6faqcfxuftfe

Learning to propagate labels on graphs: An iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction

Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Jian Xu, Xiao Xiang Zhu
2019 ISPRS journal of photogrammetry and remote sensing (Print)  
IMR aims at learning a low-dimensional subspace by jointly considering the labeled and unlabeled data, and also bridging the learned subspace with two regression tasks: labels and pseudo-labels initialized  ...  More significantly, IMR dynamically propagates the labels on a learnable graph and progressively refines pseudo-labels, yielding a well-conditioned feedback system.  ...  Acknowledgements The authors would like to thank the Hyperspectral Image Analysis group and the NSF Funded Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the CASI  ... 
doi:10.1016/j.isprsjprs.2019.09.008 pmid:31853165 pmcid:PMC6894308 fatcat:lq47lp5qnjewzh5zzpwfk2ynxm

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Pedram Ghamisi, Naoto Yokoya, Jun Li, Wenzhi Liao, Sicong Liu, Javier Plaza, Behnood Rasti, Antonio Plaza
2017 IEEE Geoscience and Remote Sensing Magazine  
This paper offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is categorized into seven broad topics: classification, spectral unmixing, dimensionality  ...  Hence, rigorous and innovative methodologies are required for hyperspectral image and signal processing and have become a center of attention for researchers worldwide.  ...  and Data Fusion Technical Committee for organizing the 2013 Data Fusion Contest.  ... 
doi:10.1109/mgrs.2017.2762087 fatcat:6ezzye7yyvacbouduqv2f2c7gi

Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods

Gustavo Camps-Valls, Devis Tuia, Lorenzo Bruzzone, Jon Atli Benediktsson
2014 IEEE Signal Processing Magazine  
However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning.  ...  This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples.  ...  His research is tied to machine learning for signal and image processing, with special focus on remote sensing data analysis.  ... 
doi:10.1109/msp.2013.2279179 fatcat:zocewzfdkbetfk4m3ocved7rpy

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing [article]

Danfeng Hong and Wei He and Naoto Yokoya and Jing Yao and Lianru Gao and Liangpei Zhang and Jocelyn Chanussot and Xiao Xiang Zhu
2021 arXiv   pre-print
However, with the ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges on reducing the burden of manual labor and improving efficiency.  ...  For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications.  ...  In [173] , a semi-supervised graph-induced aligned learning (GiAL) was developed by jointly regressing labels and pseudo-labels.  ... 
arXiv:2103.01449v1 fatcat:jvo4pr5atvfb5kohpslvkhhmky

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
and Hanssen, R.F., Incorporating Temporary Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging  ...  ., and Drake, V.A., Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix; TGRS Aug. 2019 6212-6225 Hu, C., see Zhang, M., TGRS Sept. 2019 6666-6674  ...  ., +, TGRS July 2019 4192-4201 MIMA: MAPPER-Induced Manifold Alignment for Semi-Supervised Fusion of Optical Image and Polarimetric SAR Data.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 567-576 Ideal Regularized Discriminative Multiple Kernel Subspace Alignment for Domain Adaptation in Hyperspectral Image Classification.  ...  ., +, JSTARS 2020 5704-5718 Ideal Regularized Discriminative Multiple Kernel Subspace Alignment for Domain Adaptation in Hyperspectral Image Classification.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

Hyperspectral-Multispectral Image Fusion with Weighted LASSO [article]

Nguyen Tran, Rupali Mankar, David Mayerich, Zhu Han
2020 arXiv   pre-print
We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output.  ...  Hyperspectral imaging provides superior material specificity, while multispectral images are faster to collect at greater fidelity.  ...  , Agilent Technologies University Relations Grant #3938, and the University of Houston Core for  ... 
arXiv:2003.06944v1 fatcat:j7qebddv5rhpjdfgrawifljjne

Hyperspectral Imaging and Analysis for Sparse Reconstruction and Recognition [article]

Zohaib Khan
2014 arXiv   pre-print
Hyperspectral image databases have been developed and made publicly available for further research in compressed hyperspectral imaging, forensic document analysis and spectral reflectance recovery.  ...  This thesis proposes spatio-spectral techniques for hyperspectral image analysis.  ...  PCA gives an orthogonal basis aligned with the directions of maximum variance of the data. It is useful for projecting the data onto a subspace defined by the most significant basis vectors.  ... 
arXiv:1407.7686v1 fatcat:cdx3eaoyvjaf7d6ve56c7ohv54

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 4568-4582 Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability.  ...  ., +, TIP 2020 4568-4582 Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Table of Contents

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Liu 5162 Alternating Direction Iterative Nonnegative Matrix Factorization Unmixing for Multispectral and Hyperspectral Data Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Wang, and Y. Zhang 1045 Hyperspectral Mixed Noise Removal By 1 -Norm-Based Subspace Representation . . . . . . . . . . . L. Zhuang and M. K.  ...  Qian 5682 Ideal Regularized Discriminative Multiple Kernel Subspace Alignment for Domain Adaptation in Hyperspectral Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jstars.2020.3046663 fatcat:zqzyhnzacjfdjeejvzokfy4qze

MIMA: MAPPER-Induced Manifold Alignment for Semi-Supervised Fusion of Optical Image and Polarimetric SAR Data

Jingliang Hu, Danfeng Hong, Xiao Xiang Zhu
2019 IEEE Transactions on Geoscience and Remote Sensing  
Furthermore, we propose a MAPPER-induced manifold alignment (MIMA) for the semi-supervised fusion of multi-sensory data sources.  ...  Index Terms-Hyperspectral image, MAPPER, multi-modal data fusion, multi-sensory data fusion, multispectral image, polarimetric synthetic aperture radar (PolSAR), semi-supervised manifold alignment (SSMA  ...  ACKNOWLEDGMENT The authors would like to thank the following institutions for providing data sets used in this paper: German Research Center for Geosciences, the IEEE GRSS IADTC, and Open-StreetMap.  ... 
doi:10.1109/tgrs.2019.2924113 fatcat:jx4pqvcihzcjnng3tswzuta4qm

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Jose M. Bioucas-Dias, Antonio Plaza, Gustavo Camps-Valls, Paul Scheunders, Nasser Nasrabadi, Jocelyn Chanussot
2013 IEEE Geoscience and Remote Sensing Magazine  
Very often, these applications rely on sophisticated and complex data analysis methods.  ...  The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement  ...  is considered as the benchmark anomaly detection algorithm for multispectral/hyperspectral data.  ... 
doi:10.1109/mgrs.2013.2244672 fatcat:4tk7q6izd5hevhnrck36i5wkiy

2015 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 53

2015 IEEE Transactions on Geoscience and Remote Sensing  
Carla, R., +, TGRS Dec. 2015 6344-6355 Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation.  ...  ., +, TGRS May 2015 2738-2754 Local-Manifold-Learning-Based Graph Construction for Semisupervised Hyperspectral Image Classification.  ... 
doi:10.1109/tgrs.2015.2513444 fatcat:zuklkpk4gjdxjegoym5oagotzq
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