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Using High-Resolution Airborne and Satellite Imagery to Assess Crop Growth and Yield Variability for Precision Agriculture

Chenghai Yang, James H. Everitt, Qian Du, Bin Luo, Jocelyn Chanussot
2013 Proceedings of the IEEE  
In this paper, an overview is given on the use of airborne multispectral and hyperspectral imagery and high-resolution satellite imagery for assessing crop growth and yield variability.  ...  This paper gives an overview on the use of airborne multispectral and hyperspectral imagery and high-resolution satellite imagery for assessing crop growth and yield variability.  ...  Gomez of USDA-ARS, Weslaco, TX, for acquiring the airborne imagery; W. Swanson and J. Forward of USDA-ARS, Weslaco, TX, for ground data collection and image rectification; and D. Murden, M.  ... 
doi:10.1109/jproc.2012.2196249 fatcat:gho4kovl55awpfuzdzhchjix2m

Editorial for Special Issue "Hyperspectral Imaging and Applications"

Chein-I Chang, Meiping Song, Junping Zhang, Chao-Cheng Wu
2019 Remote Sensing  
, Band Selection, Data Fusion, Applications.  ...  This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories, Data Unmixing, Spectral variability, Target Detection, Hyperspectral Image Classification  ...  time sequentially, referred to as sequential multiple band selection (SQMBS), as most traditional band selection methods do. 10-00367 Progressive Sample Processing of Band Selection for Hyperspectral  ... 
doi:10.3390/rs11172012 fatcat:c23u3rahgjhctowk5xwllt2qea

Front Matter: Volume 9874

2016 Remotely Sensed Data Compression, Communications, and Processing XII  
The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon.  ...  The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee.  ...  hyperspectral imagery 9874 0T Separate texture and structure processing for image compression 9874 0Z A nonlinear spectral unmixing method for abundance retrieval of mineral mixtures 9874 10 Real-time  ... 
doi:10.1117/12.2246638 fatcat:67cih5mf6vb6thriylnoqdar3a

A preliminary approach on the use of satellite hyperspectral data for geological mapping

G. K. Nikolakopoulos, D. A. Vaiopoulos, G. A. Skianis
2007 Bulletin of the Geological Society of Greece  
Atmospheric corrections techniques were applied to the selected Hyperion bands. The comparison of the Hyperion hyperspectral data with the JPL spectral library gave quite encouraging results.  ...  The noisiest bands were removed and sixty bands were selected for further processing.  ...  Band selection and endmember (targets) definition Hyperspectral data represents a challenge from ai data-processing point of view, as it can consist of hundreds of bands.  ... 
doi:10.12681/bgsg.17251 fatcat:pxvuzjcepravnlcslvxzxskkym

An Improved Nonlocal Sparse Unmixing Algorithm for Hyperspectral Imagery

Ruyi Feng, Yanfei Zhong, Liangpei Zhang
2015 IEEE Geoscience and Remote Sensing Letters  
As a result of the spatial consideration of the imagery, spatial sparse unmixing (SU) can improve the unmixing accuracy for hyperspectral imagery, based on the application of a spectral library and sparse  ...  Index Terms-Hyperspectral imagery, nonlocal, sparse unmixing (SU), spatial information, weight calculation.  ...  Plaza for sharing their algorithms for comparison purposes together with the AVIRIS data. They also thank the reviewers for their helpful comments.  ... 
doi:10.1109/lgrs.2014.2367028 fatcat:uowcllgk7rdg7otyabeqn56c4y

Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing

Ruyi Feng, Lizhe Wang, Yanfei Zhong
2019 Remote Sensing  
To obtain a more reliable and efficient spatial regularized sparse unmixing results, a joint local block grouping with noise-adjusted principal component analysis for hyperspectral remote-sensing imagery  ...  unmixing model in the form of sparse regression.  ...  Plaza for sharing the simulated datasets and the source code of the latest sparse algorithms with the community, together with the free downloads of the AVIRIS image.  ... 
doi:10.3390/rs11101223 fatcat:dlabi4bo4fephn3w45ie2adpe4

Fusion of Various Band Selection Methods for Hyperspectral Imagery

Yulei Wang, Lin Wang, Hongye Xie, Chein-I Chang
2019 Remote Sensing  
Two versions of BSF, called progressive BSF and simultaneous BSF, are developed for this purpose.  ...  This paper presents an approach to band selection fusion (BSF) which fuses bands produced by a set of different band selection (BS) methods for a given number of bands to be selected, nBS.  ...  The first one, used for linear spectral unmixing, was acquired by the airborne hyperspectral digital imagery collection experiment (HYDICE) sensor, and the other three popular hyperspectral images available  ... 
doi:10.3390/rs11182125 fatcat:hx6rd5fo45b2jco37njqq5zjgi

A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

Peng Zheng, Zebin Wu, Jin Sun, Yi Zhang, Yaoqin Zhu, Yuan Shen, Jiandong Yang, Zhihui Wei, Antonio Plaza
2021 Remote Sensing  
This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated  ...  The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.  ...  Acknowledgments: We are grateful to United States Geological Survey (USGS), who provided the spectral library for this research.  ... 
doi:10.3390/rs13020176 fatcat:7b5xofj425dtxboklearf4ojym

High spatial resolution spectral unmixing for mapping ash species across a complex urban environment

Jennifer Pontius, Ryan P. Hanavan, Richard A. Hallett, Bruce D. Cook, Lawrence A. Corp
2017 Remote Sensing of Environment  
This hinges on the use of endmember spectra from trees across a range of canopy condition, including the derivation of vegetation indices to inform the spectral unmixing calibration.  ...  This study uses imagery collected by the NASA Goddard LiDAR, Hyperspectral and Thermal (GLiHT) airborne imager to map ash species at the tree level in an EAB infested urban setting.  ...  We are also appreciative of Noah Ahles and technicians at the University of Vermont Spatial Analysis Laboratory for assistance with image processing.  ... 
doi:10.1016/j.rse.2017.07.027 fatcat:w4h6d3kf6femnf6se3safnvde4

Estimating the coverage of coral reef benthic communities from airborne hyperspectral remote sensing data: multiple discriminant function analysis and linear spectral unmixing

Sarah Hamylton
2011 International Journal of Remote Sensing  
Dr Tom Spencer at the University of Cambridge is thanked for guidance on the manuscript and Dr Rainer Reuter, University of Oldenburg, is thanked for assistance with fieldwork.  ...  Acknowledgements This work would not have been possible without the generous support of the Khaled bin Sultan Living Oceans Foundation.  ...  Hamylton The success of this unmixing approach implies promise for users of hyperspectral imagery to employ coarser spatial resolution imagery for underwater applications.  ... 
doi:10.1080/01431161.2011.574162 fatcat:tl6gtph66rd3nj4whniddfwhgq

Progress in Hyperspectral Remote Sensing Science and Technology in China Over the Past Three Decades

Qingxi Tong, Yongqi Xue, Lifu Zhang
2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
This paper reviews progress in hyperspectral remote sensing (HRS) in China, focusing on the past three decades.  ...  These tools such as the Hyperspectral Image Processing and Analysis System (HIPAS) incorporate a number of special algorithms and features designed to take advantage of the wealth of information contained  ...  Feature Selection and Extraction Hyperspectral sensors image objects of interest in hundreds of narrow bands that form the spectral feature space of hyperspectral data.  ... 
doi:10.1109/jstars.2013.2267204 fatcat:yu4pp5zyqzhafa4ec7m3vva7xi

2014 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 7

2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS July 2014 2811-2820 A New Band Selection Method for Hyperspectral Image Based on Data Quality.  ...  ., +, JSTARS Oct. 2014 4218-4230 Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress.  ... 
doi:10.1109/jstars.2015.2397347 fatcat:ib3tjwsjsnd6ri6kkklq5ov37a

Front Matter: Volume 6966

Proceedings of SPIE, Sylvia S. Shen, Paul E. Lewis
2008 Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV  
Publication of record for individual papers is online in the SPIE Digital Library.  ...  Downloaded From: https://www.spiedigitallibrary.org/conference-proceedings-of-spie on 7/19/2018 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use  ...  (USA) 6966 06 Band selection for hyperspectral target detection based on a multinormal mixture anomaly detection algorithm [6966-05] I. Kåsen, A. Rødningsby, T. V. Haavardsholm, T.  ... 
doi:10.1117/12.801998 fatcat:rt2tub26ezcavdqt6uztn4g3hm

Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Ruyi Feng, Yanfei Zhong, Lizhe Wang, Wenjuan Lin
2017 Remote Sensing  
Sparse unmixing, as one of the typical semi-supervised spectral unmixing methods, reformulates the linear spectral unmixing problem as selecting endmembers from a standard spectral library using sparse  ...  and recover the different levels of important structures and details in the hyperspectral remote sensing image unmixing procedure, as the different levels of structures and edges in remote sensing imagery  ...  Ma for sharing their latest sparse unmixing algorithm source code and their good suggestions as to how we could improve our paper.  ... 
doi:10.3390/rs9121218 fatcat:no56rbuypnfztdi6qrdlnqrcra

Nonnegative matrix factorization for hyperspectral unmixing using prior knowledge of spectral signatures

Wei Tang
2012 Optical Engineering: The Journal of SPIE  
Hyperspectral unmixing is a process aiming at identifying the constituent materials and estimating the corresponding fractions from hyperspectral imagery of a scene.  ...  As the data of hyperspectral imagery analyzed deeper, prior knowledge of some signatures in the scene could be available.  ...  NMF for hyperspectral unmixing using prior knowledge (NMFupk) NMFupk is developed for hyperspectral unmixing with prior knowledge of spectral signatures in the scene.  ... 
doi:10.1117/1.oe.51.8.087001 fatcat:thigvb7rvfedhnvxckyvswbyue
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