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Deep Learning-Based Approach for Estimation of Fractional Abundance of Nitrogen in Soil from Hyperspectral Data
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Objective of this research is to explore the use of a deep learning network to estimate the abundance of urea fertilizer mixed soils for spectroradiometer data. ...
The results show that the estimated abundances obtained through the derivative analysis for spectral unmixing (DASU)-based deep learning network, facilitated a greater accuracy in comparison to the sole ...
Consequently, this will reduce the time and cost Deep Learning-Based Approach for Estimation of Fractional Abundance of Nitrogen in Soil from Hyperspectral Data Ajay Kumar Patel, Jayanta Kumar Ghosh, Shivam ...
doi:10.1109/jstars.2020.3039844
fatcat:iokpnq2t5bhwvkydtbkwmfxpri
2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12
2019
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., +, JSTARS Nov. 2019 4351-4360 Deep learning A Super-Resolution Convolutional-Neural-Network-Based Approach for Subpixel Mapping of Hyperspectral Images. ...
., +, JSTARS Dec. 2019 4773-4786 Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains. ...
doi:10.1109/jstars.2020.2973794
fatcat:sncrozq3fjg4bgjf4lnkslbz3u
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
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., +, JSTARS 2020 2200-2213 Deep-Learning-Based Approach for Estimation of Fractional Abundance of Nitrogen in Soil From Hyperspectral Data. ...
., +, JSTARS 2020 2899-2915
Deep-Learning-Based Approach for Estimation of Fractional Abundance of
Nitrogen in Soil From Hyperspectral Data. ...
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
Machine learning based hyperspectral image analysis: A survey
[article]
2019
arXiv
pre-print
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This paper reviews and compares recent machine learning-based hyperspectral image analysis methods published in literature. ...
Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment ...
[44] covers biophysical parameter estimation using GPs from hyperspectral imagery in detail. In a different approach, Murphy et al. ...
arXiv:1802.08701v2
fatcat:bfi6qkpx2bf6bowhyloj2duugu
Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming
2021
Remote Sensing
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Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. ...
In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for ...
Fruit weight and yield estimation were also discussed, which demonstrates the superiority of deep learning in analyzing multi-dimensional remote sensing data. ...
doi:10.3390/rs13030531
fatcat:yts5pbuq2zhwrm6rt6c6hmkyti
2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57
2019
IEEE Transactions on Geoscience and Remote Sensing
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., 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 Hu, C., Zhang, ...
Polar Format Algorithm for Curvilinear Spotlight SAR Imaging on Arbitrary Region of Interest; TGRS Oct. 2019 7995-8010 Hu, T., see Kang, Z., TGRS Jan. 2019 181-193 Hu, T., Wu, Y., Zheng, G., Zhang, ...
., +, TGRS Jan. 2019 46-61 The Value of SMAP for Long-Term Soil Moisture Estimation With the Help of Deep Learning. ...
doi:10.1109/tgrs.2020.2967201
fatcat:kpfxoidv5bgcfo36zfsnxe4aj4
Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
[article]
2021
arXiv
pre-print
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This resulted in the development of algorithms that incorporate different strategies to allow the EMs to vary within a hyperspectral image, using, for instance, sets of spectral signatures known a priori ...
We also review methods used to construct spectral libraries (which are required by many SU techniques) based on the observed hyperspectral image, as well as algorithms for library augmentation and reduction ...
A later approach for SU of soil and vegetation mixtures proposed to estimate the biophysical parameters blindly from the hyperspectral image using the PROSAIL model for vegetation spectra [205] . ...
arXiv:2001.07307v3
fatcat:6ambb6x2pzgoxott3jqt3hts2i
Integration of crop growth model and random forest for winter wheat yield estimation from UAV hyperspectral imagery
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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In conclusion, this study showed that the CERES-Wheat model simulation can be important data source for machine learning-based wheat yield estimation model at field plot scale, and the hyperspectral sensor ...
In addition, the UAV hyperspectral data were found to significantly improve the retrieval accuracy, and further improve CW-RF model estimation accuracy. ...
quality and presentation of this article. ...
doi:10.1109/jstars.2021.3089203
fatcat:eog3tfykv5g6nodr7bchbqseuu
Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm
2021
PLoS ONE
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Based on the above results, a new method is proposed in this study for band selection in the early phase of soil hyperspectral modeling. ...
This study provides a reference for the remote sensing of soil fertility in forests of different soil types and a theoretical basis for developing portable equipment for the hyperspectral measurement of ...
Acknowledgments We thank each editor and the anonymous reviewers for their insightful comments, which helped in the publication of this manuscript. ...
doi:10.1371/journal.pone.0253385
pmid:34181687
pmcid:PMC8238212
fatcat:q4nc6yakvfftrnfkrac2hb2htu
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
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., +, JSTARS June 2014 2246-2255 Deep Learning-Based Classification of Hyperspectral Data. ...
., +, JSTARS Aug. 2014 3525-3533 Deep Learning-Based Classification of Hyperspectral Data. ...
doi:10.1109/jstars.2015.2397347
fatcat:ib3tjwsjsnd6ri6kkklq5ov37a
A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses
2019
Drones
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Unmanned aerial vehicles (UAVs) are becoming a valuable tool to collect data in a variety of contexts. ...
Indeed, the use of UAVs for monitoring and assessing crops, orchards, and forests has been growing steadily during the last decade, especially for the management of stresses such as water, diseases, nutrition ...
Conflicts of Interest: The author declares no conflict of interest. ...
doi:10.3390/drones3020040
fatcat:33jjpgn6rnanxgrnu7235ooymy
Vegetation Cover Analysis of Hazardous Waste Sites in Utah and Arizona Using Hyperspectral Remote Sensing
2012
Remote Sensing
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However, it is believed that the vegetation mapping would benefit from the use of higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (<1 m) found on the ...
This study investigated the usability of hyperspectral remote sensing for characterizing vegetation at hazardous waste sites. ...
Acknowledgements This research was funded by the Department of Energy.
References and Notes ...
doi:10.3390/rs4020327
fatcat:4tgldaxd5faarjkr3ptp66yqga
The self-supervised spectral-spatial attention-based transformer network for automated, accurate prediction of crop nitrogen status from UAV imagery
[article]
2022
arXiv
pre-print
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In this work, we propose a novel deep learning framework: a self-supervised spectral-spatial attention-based vision transformer (SSVT). ...
The proposed approach achieved high accuracy (0.96) with good generalizability and reproducibility for wheat N status estimation. ...
ACKNOWLEDGMENT The work reported in this paper has formed part of the N2Vision project funded by UKRI-ISCF-TFP (Grant no. 134063). ...
arXiv:2111.06839v2
fatcat:mnoeuqfkzvbmpouusbkrfzn3qy
The Self-Supervised Spectral–Spatial Vision Transformer Network for Accurate Prediction of Wheat Nitrogen Status from UAV Imagery
2022
Remote Sensing
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In this work, we propose a novel deep learning framework: a self-supervised spectral–spatial attention-based vision transformer (SSVT). ...
The proposed approach achieved high accuracy (0.96) with good generalizability and reproducibility for wheat N status estimation. ...
Acknowledgments: We thank the anonymous reviewers for reviewing the manuscript and providing comments to improve the manuscript. ...
doi:10.3390/rs14061400
fatcat:spcgmlwobvaf5arjlam6oitwi4
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry
2017
Remote Sensing
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With the goal of simplifying hyperspectral data processing-by isolating the common user from the processes' mathematical complexity-several available toolboxes that allow a direct access to level-one hyperspectral ...
Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops ...
Alternatively to the presented hyperspectral data processing, emerging approaches for dealing with HSI complexity based on deep learning (DL) are worthy to be referred. ...
doi:10.3390/rs9111110
fatcat:hfvvft56afbprjmgfqflizqgj4
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