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Zhang 157 Unsupervised Bayesian Subpixel Mapping of Hyperspectral Imagery Based on Band-Weighted Discrete Spectral Mixture ...
Model and Markov Random Field .................................................................................... ................................................... ...
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Machine learning based hyperspectral image analysis: A survey
[article]
<span title="2019-02-10">2019</span>
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arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2018-02-23">2018</span>
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We also discuss the open challenges in the field of hyperspectral image analysis and explore possible future directions. ...
This paper reviews and compares recent machine learning-based hyperspectral image analysis methods published in literature. ...
[97] proposed using Markov random field and hierarchical Bayesian linear unmixing for simultaneous spatial-spectral unmixing and classification of a hyperspectral image. ...
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2015 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 53
<span title="">2015</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4odsbtjobjalfki6xxabjpdu6y" style="color: black;">IEEE Transactions on Geoscience and Remote Sensing</a>
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., +, An Adaptive Subpixel Mapping Method Based on MAP Model and Class Determination Strategy for Hyperspectral Remote Sensing Imagery. ...
., +, TGRS May 2015 2532-2546 Supervised Spectral-Spatial Hyperspectral Image Classification With Weighted Markov Random Fields. ...
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2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57
<span title="">2019</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4odsbtjobjalfki6xxabjpdu6y" style="color: black;">IEEE Transactions on Geoscience and Remote Sensing</a>
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., 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 ...
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, D., Zhang, Y., and Li, Y., Tropical Cyclone Center ...
., +, TGRS April 2019 2187-2197
Markov Random Fields Integrating Adaptive Interclass-Pair Penalty and
Spectral Similarity for Hyperspectral Image Classification. ...
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2014 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 7
<span title="">2014</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b2n2tpw5ang73osulebz6bm4ju" style="color: black;">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</a>
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., and Foerster, S ...
., +, JSTARS May 2014 1816-1825 Super-Resolution Mapping of Forests With Bitemporal Different Spatial Resolution Images Based on the Spatial-Temporal Markov Random Field. ...
., +, JSTARS May 2014 1816-1825 Super-Resolution Mapping of Forests With Bitemporal Different Spatial Resolution Images Based on the Spatial-Temporal Markov Random Field. ...
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Chronological Advancement in Image Processing from Lime Stone Mofits to Superpixel Classification
<span title="2015-11-01">2015</span>
<i title="Science Publications">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wake4w3hqzd4ndiy2zowpciv64" style="color: black;">Journal of Computer Science</a>
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While extracting the information from the remote sensed images the major issues that affect the accuracy of the classification is the presence of mixed pixels (reflecting more than one spectral signature ...
In this study, a new algorithm is proposed to solve the mixed pixel problem by using PSO based Fuzzy C-mean and Biogeographical Based Optimization (BBO). ...
Li et al. (2014) proposed a new model based on spatial-temporal Markov Random Field (MRF) and super Resolution Mapping (STMRF_SRM). ...
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Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art
<span title="">2017</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xwpybjcqyffjdipbdviyet75c4" style="color: black;">IEEE Geoscience and Remote Sensing Magazine</a>
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This paper offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is categorized into seven broad topics: classification, spectral unmixing, dimensionality ...
Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information, which make a plethora of applications for the ...
In addition, the authors would like to thank the National Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the CASI Houston data set, and the IEEE GRSS Image Analysis ...
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2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12
<span title="">2019</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b2n2tpw5ang73osulebz6bm4ju" style="color: black;">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</a>
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., and Lopez, J.F ...
., +, JSTARS Dec. 2019 5086-5100 Spectral-Spatial Hyperspectral Image Classification Using Cascaded Markov Random Fields. ...
., +, JSTARS Jan. 2019 210-222 A Hybrid Markov Random Field Model With Multi-Granularity Information for Semantic Segmentation of Remote Sensing Imagery. ...
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Hyperspectral Remote Sensing Data Analysis and Future Challenges
<span title="">2013</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xwpybjcqyffjdipbdviyet75c4" style="color: black;">IEEE Geoscience and Remote Sensing Magazine</a>
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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 ...
Very often, these applications rely on sophisticated and complex data analysis methods. ...
Spatio-spectral methods that were developed use e.g. wavelet transforms to fuse multiresolution information of the image bands [21] , Markov Random Fields that model the spatial relationship between neighboring ...
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A survey of image classification methods and techniques for improving classification performance
<span title="">2007</span>
<i title="Informa UK Limited">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/t3jxbtntvjhlnbvwdasscriu4u" style="color: black;">International Journal of Remote Sensing</a>
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The foci of this paper are on providing a summarization of major advanced classification methods and techniques used for improving classification accuracy, and on discussing important issues affecting ...
The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. ...
Acknowledgments The authors wish to acknowledge the support from the Center for the Study of Institutions, Population, and Environmental Change (CIPEC) at Indiana University, through funding from the National ...
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Support vector machines in remote sensing: A review
<span title="">2011</span>
<i title="Elsevier BV">
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A wide range of methods for analysis of airborne-and satellite-derived imagery continues to be proposed and assessed. ...
A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). ...
of Excellence CARTI Program. ...
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Multimodal Classification of Remote Sensing Images: A Review and Future Directions
<span title="">2015</span>
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We also highlight the most recent advances, which exploit synergies with machine learning and signal processing: sparse methods, kernel-based fusion, 2 Markov modeling, and manifold alignment. ...
image fusion and multidimensional interpolation among sensors of different spatial, spectral and temporal resolutions; 3) multiangular image classification; 4) multi-sensor image fusion exploiting physically-based ...
ACKNOWLEDGEMENTS The authors would like to thank DigitalGlobe Inc. for the optical data on Rio and Haiti, and the Italian Space Agency for the SAR data on Haiti. ...
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Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes
<span title="">2021</span>
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(AMD) mixtures deposited on three beaches of Chiloé island. ...
The first convolutional layer computes 64 feature maps using a 9 px × 9 px receptive field (patch size) and the second computes 32 feature maps with a 5 px × 5 px kernel size. ...
Since 2019, he has been the Head of the Geo-analytics design team of Planetek Italia, pushing the most advanced and state-of-theart solutions for providing added-value products and services based on remote ...
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A model-based approach to hyperspectral change detection
<span title="2010-04-23">2010</span>
<i title="SPIE">
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
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Change detection refers to the problem of identifying interesting changes that occur to a spatial area over which imagery has been collected on multiple occasions. ...
Within the hyperspectral community, change detection is a continued area of interest. ...
This Bayesian approach would utilize Markov random field concepts and essentially apply a hypothesis test to groups of pixels to determine if the area is spatially uniform. ...
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Hyperspectral Image Classification: Potentials, Challenges, and Future Directions
<span title="2022-04-28">2022</span>
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Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. ...
This article presents an extensive survey depicting machine-dependent technologies' contributions and deep learning on landcover classification based on hyperspectral images. ...
of Korea (NRF) grant funded by the Korea government (MSIT) (Grant no. 2022R1C1C1004590). ...
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