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1
Detecting changes between optical images of different spatial and spectral resolutions: a fusion-based approach
Detecting changes between optical images of different spatial and spectral resolutions: a fusion-based approach
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Detecting changes between optical images of different spatial and spectral resolutions: a fusion-based approach

IEEE transactions on geoscience and remote sensing, 2018-03, Vol.56 (3), p.1566-1578 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0196-2892 ;EISSN: 1558-0644 ;DOI: 10.1109/TGRS.2017.2765348

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2
Sustainable Agriculture and Advances of Remote Sensing (Volume 1)
Sustainable Agriculture and Advances of Remote Sensing (Volume 1)
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Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

ISBN: 9783036553382 ;ISBN: 9783036553375 ;ISBN: 3036553371 ;ISBN: 303655338X ;DOI: 10.3390/books978-3-0365-5338-2

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3
Sustainable Agriculture and Advances of Remote Sensing (Volume 2)
Sustainable Agriculture and Advances of Remote Sensing (Volume 2)
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Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

ISBN: 9783036553368 ;ISBN: 9783036553351 ;ISBN: 3036553363 ;ISBN: 3036553355 ;DOI: 10.3390/books978-3-0365-5336-8

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4
Nitrogen prediction model of rice plant at panicle initiation stage using ground-based hyperspectral imaging: growing degree-days integrated model
Nitrogen prediction model of rice plant at panicle initiation stage using ground-based hyperspectral imaging: growing degree-days integrated model
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Nitrogen prediction model of rice plant at panicle initiation stage using ground-based hyperspectral imaging: growing degree-days integrated model

Precision agriculture, 2015-10, Vol.16 (5), p.558-570 [Peer Reviewed Journal]

Springer Science+Business Media New York 2015 ;ISSN: 1385-2256 ;EISSN: 1573-1618 ;DOI: 10.1007/s11119-015-9394-9

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5
CSP - Hyperspectral Imaging and Sounding of the Environment Meeting Scholarship Fund
CSP - Hyperspectral Imaging and Sounding of the Environment Meeting Scholarship Fund
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CSP - Hyperspectral Imaging and Sounding of the Environment Meeting Scholarship Fund

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6
Recent Advances in Compressed Sensing: Discrete Uncertainty Principles and Fast Hyperspectral Imaging
Recent Advances in Compressed Sensing: Discrete Uncertainty Principles and Fast Hyperspectral Imaging
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Recent Advances in Compressed Sensing: Discrete Uncertainty Principles and Fast Hyperspectral Imaging

Approved for public release; distribution is unlimited.

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7
A Polarimetric Hyperspectral Imaging Sensor
A Polarimetric Hyperspectral Imaging Sensor
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A Polarimetric Hyperspectral Imaging Sensor

Applications of Photonic Technology, p.183-189

Springer Science+Business Media New York 1995 ;ISBN: 9781475792492 ;ISBN: 1475792492 ;EISBN: 1475792476 ;EISBN: 9781475792478 ;DOI: 10.1007/978-1-4757-9247-8_36

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8
Detection and Classification from Hyperspectral Imagery Using the Normal Compositional Model
Detection and Classification from Hyperspectral Imagery Using the Normal Compositional Model
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Detection and Classification from Hyperspectral Imagery Using the Normal Compositional Model

APPROVED FOR PUBLIC RELEASE

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9
The Influence of Bubbles on the Backscattering of Spectral Irradiance in the Upper Ocean
The Influence of Bubbles on the Backscattering of Spectral Irradiance in the Upper Ocean
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The Influence of Bubbles on the Backscattering of Spectral Irradiance in the Upper Ocean

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10
Potential Payoff of Fusion Between HSI and Other Sensors
Potential Payoff of Fusion Between HSI and Other Sensors
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Potential Payoff of Fusion Between HSI and Other Sensors

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11
Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization
Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization
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Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization

IEEE transactions on image processing, 2015-12, Vol.24 (n° 12) [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1057-7149 ;EISSN: 1941-0042 ;DOI: 10.1109/TIP.2015.2468177

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12
Hyperspectral super-resolution of locally low rank images from complementary multisource data
Hyperspectral super-resolution of locally low rank images from complementary multisource data
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Hyperspectral super-resolution of locally low rank images from complementary multisource data

IEEE transactions on image processing, 2016-01, Vol.25 (1) [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1057-7149 ;EISSN: 1941-0042 ;DOI: 10.1109/TIP.2015.2496263

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13
Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
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Hyperspectral unmixing with spectral variability using a perturbed linear mixing model

IEEE transactions on signal processing, 2016-01, Vol.64 (2), p.525-538 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1053-587X ;EISSN: 1941-0476 ;DOI: 10.1109/TSP.2015.2486746

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14
Nonlinear unmixing of hyperspectral images using a generalized bilinear model
Nonlinear unmixing of hyperspectral images using a generalized bilinear model
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Nonlinear unmixing of hyperspectral images using a generalized bilinear model

IEEE transactions on geoscience and remote sensing, 2011-11, Vol.49 (11), p.4153-4162 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0196-2892 ;EISSN: 1558-0644 ;DOI: 10.1109/TGRS.2010.2098414

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15
A Tensor Decomposition-Based Anomaly Detection Algorithm for Hyperspectral Image
A Tensor Decomposition-Based Anomaly Detection Algorithm for Hyperspectral Image
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A Tensor Decomposition-Based Anomaly Detection Algorithm for Hyperspectral Image

IEEE transactions on geoscience and remote sensing, 2016-10, Vol.54 (10), p.5801-5820 [Peer Reviewed Journal]

ISSN: 0196-2892 ;EISSN: 1558-0644 ;DOI: 10.1109/TGRS.2016.2572400 ;CODEN: IGRSD2

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16
Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review
Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review
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Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review

International journal of applied earth observation and geoinformation, 2021-12, Vol.105, p.102603, Article 102603 [Peer Reviewed Journal]

2021 The Authors ;ISSN: 1569-8432 ;EISSN: 1872-826X ;DOI: 10.1016/j.jag.2021.102603

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17
The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination
The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination
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The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination

Sensors (Basel, Switzerland), 2021-02, Vol.21 (4), p.1182 [Peer Reviewed Journal]

2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 by the authors. 2021 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s21041182 ;PMID: 33567591

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18
Spectral Shift Mitigation for Cross-Scene Hyperspectral Imagery Classification
Spectral Shift Mitigation for Cross-Scene Hyperspectral Imagery Classification
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Spectral Shift Mitigation for Cross-Scene Hyperspectral Imagery Classification

IEEE journal of selected topics in applied earth observations and remote sensing, 2021, Vol.14, p.6624-6638 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 ;ISSN: 1939-1404 ;EISSN: 2151-1535 ;DOI: 10.1109/JSTARS.2021.3091591 ;CODEN: IJSTHZ

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19
Hyperspectral Image Classification Using Convolutional Neural Networks and Multiple Feature Learning
Hyperspectral Image Classification Using Convolutional Neural Networks and Multiple Feature Learning
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Hyperspectral Image Classification Using Convolutional Neural Networks and Multiple Feature Learning

Remote sensing (Basel, Switzerland), 2018-02, Vol.10 (2), p.299 [Peer Reviewed Journal]

Copyright MDPI AG 2018 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs10020299

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20
A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery
A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery
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A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery

Remote sensing (Basel, Switzerland), 2019-10, Vol.11 (19), p.2326 [Peer Reviewed Journal]

2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11192326

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