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A survey of image classification methods and techniques for improving classification performance
2007
International Journal of Remote Sensing
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Remote-sensing classification process Remote-sensing classification is a complex process and requires consideration of many factors. ...
Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. ...
Improving classification performance 849 Downloaded by [207.241.229.243] at 11:53 05 November 2017 ...
doi:10.1080/01431160600746456
fatcat:xs7y7x4bpfhnfn5ahesllpchei
Incorporating uncertanity into Markov random field classification with the combine use of optical and SAR images and aduptive fuzzy mean vector
2014
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The model uses the contextual information from the optical image pixels and the SAR pixel intensity with corresponding fuzzy grade of memberships respectively, in the classification mechanism. ...
A Markov Random Field (MRF) model accounting for the classification uncertainty using multisource satellite images and an adaptive fuzzy class mean vector is proposed in this study. ...
ACKNOWLEDGEMENTS The authors would like to thank the reviewers for their constructive comments and Alexandros Kordonis, Shengye Jin and Dinesh Chandana of the Graduate School of Engineering, Kyoto University ...
doi:10.5194/isprsannals-ii-7-89-2014
fatcat:huu6aejxonhb3nb7edkccfrgcy
Data Fusion for Remote-Sensing Applications
[chapter]
2006
Signal and Image Processing for Remote Sensing
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The main focus is on methods for multisource, multiscale and multitemporal image classification. ...
We present and discuss methods for multisource image analysis and provide a tutorial on the subject on data fusion for remote sensing. ...
Acknowledgements The author would like to thank Line Eikvil for valuable input, in particular regarding multisensor image registration. ...
doi:10.1201/9781420003130.ch23
fatcat:gln4jphaxrgg7dilrw2oyuxvra
Multisource Clustering Of Remote Sensing Images With Entropy-Based Dempster-Shafer Fusion
2013
Zenodo
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Publication in the conference proceedings of EUSIPCO, Marrakech, Morocco, 2013 ...
The DS theory has been successfully applied to the segmentation fusion of biomedical images [7] and the classification fusion of remote sensing images [4] , [8] . ...
Fusion of different classifiers [2] or of classifications from multiple sources [3] have also received wide attention in remote sensing. ...
doi:10.5281/zenodo.43368
fatcat:wwspszy4srf7tfr4a6crinarza
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection
2008
IEEE Transactions on Geoscience and Remote Sensing
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The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can ...
First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. ...
ACKNOWLEDGMENT The authors would like to thank the ESA for the availability of the image database and Dr. D. ...
doi:10.1109/tgrs.2008.916201
fatcat:bbyznfvm5zao3g7aoaogllih34
Machine learning in remote sensing data processing
2009
2009 IEEE International Workshop on Machine Learning for Signal Processing
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This paper serves as a survey of methods and applications, and reviews the latest methodological advances in machine learning for remote sensing data analysis. ...
For instance urban monitoring, fire detection or flood prediction from remotely sensed multispectral or radar images have a great impact on economical and environmental issues. ...
Image classification Classification maps are the main product of remote sensing image processing. In the last years, data-driven approaches have gained relevance in the remote sensing community. ...
doi:10.1109/mlsp.2009.5306233
fatcat:tb3on4evwvdvpkri67dbph7zfy
Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments
2021
Proceedings of the IEEE
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ABSTRACT | Despite the wide availability of remote sensing big data from numerous different Earth Observation (EO) instruments, the limitations in the spatial and temporal resolution of such EO sensors ...
(as well as atmospheric opacity and other kinds of interferers) have led to many situations in which using only remote sensing data cannot fully meet the requirements of applications in which a (near) ...
The paper presented in [60] proposed a fuzzy approach to explore the inherent ambiguity of remote sensing data and ground data for the classification of suburban land cover. ...
doi:10.1109/jproc.2021.3079176
fatcat:gk2xqgsipjfr7kfanauymtk724
Decision Fusion for the Classification of Urban Remote Sensing Images
2006
IEEE Transactions on Geoscience and Remote Sensing
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The classification of very high-resolution remote sensing images from urban areas is addressed by considering the fusion of multiple classifiers which provide redundant or complementary results. ...
By modeling the output of a classifier as a fuzzy set, this point-wise reliability is defined as the degree of uncertainty of the fuzzy set. ...
ACKNOWLEDGMENT This research was supported in part by the Research Fund of the University of Iceland and the Jules Verne Program of the French and Icelandic governments (PAI EGIDE). ...
doi:10.1109/tgrs.2006.876708
fatcat:lredbbctlfa4jpbvtslvz4yxr4
FOREST RESOURCES STUDY IN MONGOLIA USING ADVANCED SPATIAL TECHNOLOGIES
2012
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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As remote sensing (RS) data sources, panchromatic and multispectral Landsat 7 images as well as ALOS PALSAR L-band HH polarization data are used. ...
The aim of this study is to conduct a forest resources study using optical and synthetic aperture radar (SAR) satellite images. ...
To increase the reliability of the classification, to the initially classified images, a fuzzy convolution with a 3x3 size window was applied. ...
doi:10.5194/isprsarchives-xxxix-b7-257-2012
fatcat:buqhqxrwbzac5cvvmsiawchfcy
Multitemporal/multiband sar classification of urban areas using spatial analysis: statistical versus neural kernel-based approach
2003
IEEE Transactions on Geoscience and Remote Sensing
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In this paper, we derive two techniques for the classification of multifrequency/multitemporal polarimetric SAR images, based respectively on a statistical and on a neural approach. ...
They are applied to a set of SIR-C images of a urban area, to test their effectiveness in the identification of the different classes that compose the observed scene. ...
Macrì Pellizzeri are grateful to InfoSAR-Liverpool, UK for making available the InfoPACK SAR image processing software. They also thank C. J. ...
doi:10.1109/tgrs.2003.818762
fatcat:q5tjljphbnf55nmsxasws6uheu
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 847-858 A Contextual Bidirectional Enhancement Method for Remote Sensing Image Object Detection. ...
., +, JSTARS 2020 4044-4059 A Contextual Bidirectional Enhancement Method for Remote Sensing Image Object Detection. ...
doi:10.1109/jstars.2021.3050695
fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
Editorial to Special Issue "Multispectral Image Acquisition, Processing, and Analysis"
2019
Remote Sensing
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Conflicts of Interest: The authors declare no conflict of interest. ...
information from remote sensing data. ...
detection, and domain adaptation. (3) Multisource data fusion: optical-radar fusion and pan-sharpening; field sensing; crowd sensing. ...
doi:10.3390/rs11192310
fatcat:f43hp2ixcnhiblfqi4d4355ijm
Object-Based Classification of Urban Areas Using VHR Imagery and Height Points Ancillary Data
2012
Remote Sensing
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OPEN ACCESS Remote Sens. 2012, 4 2257 ...
Results show an overall accuracy of 92% and 86% and a Kappa coefficient of 0.88 and 0.80 for the QB and IK Test image, respectively. ...
Acknowledgment The authors would like to thank the City of Fredericton for providing the data used in this work. ...
doi:10.3390/rs4082256
fatcat:zzf2i4xodvfmphqhtfdhbginye
Multiple Classifier System for Remote Sensing Image Classification: A Review
2012
Sensors
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Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. ...
Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple ...
(PAPD) and Natural Science Foundation of Jiangsu Province, China (BK2010182). ...
doi:10.3390/s120404764
pmid:22666057
pmcid:PMC3355439
fatcat:xeu6ougwsrhfpfoyvl4llqxw2m
Landslide Extraction from High-Resolution Remote Sensing Imagery Using Fully Convolutional Spectral–Topographic Fusion Network
2021
Remote Sensing
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images of Resources Satellite-3 and multi-source high-resolution remote sensing image data (Beijing-2, Worldview-3, and SuperView-1). ...
In this study, comprehensive research was carried out on the landslide features of high-resolution remote sensing images on the Mangkam dataset. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs13245116
fatcat:mrppmxgicbcdrkrheyptqf2oqi
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