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Hyperspectral Image Classification by Fusion of Multiple Classifiers
2016
International Journal of Database Theory and Application
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Secondly, due to lack of labeled training sample points, this paper proposes a new algorithm that combines support vector machines and Bayesian classifier to create a discriminative/generative hyperspectral ...
Hyperspectral image mostly have very large amounts of data which makes the computational cost and subsequent classification task a difficult issue. ...
Therefore, based our former research works [8] [9] [10] [11] , this paper propose a new framework for hyperspectral image classification, firstly, spectral clustering is used to select effective bands ...
doi:10.14257/ijdta.2016.9.2.20
fatcat:ymsfuojsqzevbhqh2cew5gfgpa
Editorial for Special Issue "Hyperspectral Imaging and Applications"
2019
Remote Sensing
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, Band Selection, Data Fusion, Applications. ...
The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore ...
(BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one band at a ...
doi:10.3390/rs11172012
fatcat:c23u3rahgjhctowk5xwllt2qea
Spectral partitioning for hyperspectral remote sensing image classification
2014
2014 IEEE Geoscience and Remote Sensing Symposium
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Then, a multiple classifier system (MCS) based on multinomial logistic regression (MLR) is applied. ...
The system is trained using different band subsets resulting from the previously conducted intelligent grouping, and the results are combined to produce a final classification result. ...
Then, a multiple classifier system (MCS) based on multinomial logistic regression (MLR) is applied. ...
doi:10.1109/igarss.2014.6947220
dblp:conf/igarss/LiuLPBCR14
fatcat:xypqof32lfdttci5spz6xli4ye
Dynamic Ensemble Selection Approach for Hyperspectral Image Classification With Joint Spectral and Spatial Information
2015
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Paolo Gamba, Department of Electronics, University of Pavia, Italy for providing us with ROSIS hyperspectral image and ground truth map used in this study. B. B. ...
The highly constructive criticism and suggestions of the associate editor and anonymous reviewers are gratefully acknowledged. ...
In order to have an accurate as well as computationally fast DCS/DES, we proposed a new DCS/DES framework based on extreme learning machine (ELM) regression and a new spectral-spatial classification model ...
doi:10.1109/jstars.2015.2407493
fatcat:kspqkpls4feffffoxwklr47dgq
A survey of band selection techniques for hyperspectral image classification
2020
Journal of Spectral Imaging
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Focusing on the classification task, this article provides an extensive and comprehensive survey on band selection techniques describing the categorisation of methods, methodology used, different searching ...
Band selection is an effective way to reduce the size of hyperspectral data and to overcome the curse of the dimensionality problem in ground object classification. ...
Acknowledgement The authors thank the Council of Scientific & Industrial Research (CSIR), New Delhi, India, for the award of CSIR-SRF and the Vellore Institute of Technology (VIT) for providing a VIT seed ...
doi:10.1255/jsi.2020.a5
fatcat:cvibjoofbbd6jpu4ij626wigdy
Assessing the performance of multiple spectral–spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network
2017
Journal of Applied Remote Sensing
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Consequently, approaches with a combination of spectral and spatial information in a single classification framework have attracted special attention because of their potential to improve the classification ...
We tested the proposed method on a real application of hyperspectral image acquired from AisaFENIX and also on widely used hyperspectral images (ROSIS and AVIRIS). ...
Acknowledgment This research was supported by Massey University, Palmerston North, New Zealand. The authors also would like to thank Aerial Surveys, New Zealand for providing aerial service. ...
doi:10.1117/1.jrs.11.026009
fatcat:apc5l5hrnfeqdhea4g7cdgjb7y
Unsupervised classifier selection approach for hyperspectral image classification
2016
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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Generating accurate and robust classification maps from hyperspectral imagery (HSI) depends on the users choice of the classifiers and input data sources. ...
In this paper, we propose a unsupervised classifier selection strategy to select an appropriate subset of accurate classifiers for the multiple classifier combination from a large pool of classifiers. ...
Landgrebe and Prof. P. Gamba for providing AVIRIS and ROSIS images. ...
doi:10.1109/igarss.2016.7730332
dblp:conf/igarss/DamodaranCL16
fatcat:2lhf425nlzaldhlituvqf2wjty
An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques
2022
Journal of Spectral Imaging
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As a result, this paper reviews three different types of hyperspectral image machine learning classification methods: cluster analysis, supervised and semi-supervised classification. ...
Hyperspectral imaging is used in a wide range of applications. ...
Images can be classified as multiple classifiers, contextual, knowledge-based, per-field, sub-pixel and per-pixel based on pixel information. ...
doi:10.1255/jsi.2022.a1
fatcat:rue5klkmlfcrzftepc6lzfcbfe
Dynamic classifier selection using spectral-spatial information for hyperspectral image classification
2014
Journal of Applied Remote Sensing
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This paper presents a new dynamic classifier selection approach for hyperspectral image classification, in which both spatial and spectral information are used to determine a pixel's label once the remaining ...
For volumetric texture feature extraction, a volumetric gray level co-occurrence matrix is used; for spectral feature extraction, a minimum estimated abundance covariance-based band selection is used. ...
based on priori selection and posteriori selection methods was proposed. ...
doi:10.1117/1.jrs.8.085095
fatcat:au7lw2j5c5dylcqsl7pceit3kq
Multiple Classifiers and Graph Cut Methods for Spectral Spatial Classification of Hyperspectral Image
2014
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The multiple classifier system (MCS) has the potential to increase the classification accuracy and reliability of the hyperspectral image. ...
Supervised classification is the one of method used to exploit the information from the hyperspectral image. ...
Paolo Gamba, Department of Electronics, University of Pavia, Italy for providing us with RO-SIS hyperspectral images and ground truth map used in this study. ...
doi:10.5194/isprsarchives-xl-8-683-2014
fatcat:nmoy6zkkrva5ng5k6ddasucqtq
Dimension reduction and classification of hyperspectral images based on neural network sensitivity analysis and multi-instance learning
2019
Computer Science and Information Systems
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In our proposed method, we combined neural network sensitivity analysis with a multiinstance learning algorithm based on a support vector machine to achieve dimension reduction and accurate classification ...
Hyperspectral remote image sensing is a rapidly developing integrated technology used widely in numerous areas. ...
Therefore, Ruck sensitivity analysis is used to study band selection for a hyperspectral remote sensing image based on a BP neural network classifier. ...
doi:10.2298/csis180428003l
fatcat:gpakb2jwwvesdgfxvasoor2eoq
Light detection and ranging and hyperspectral data for estimation of forest biomass: a review
2014
Journal of Applied Remote Sensing
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A review on the status of hyperspectral data, LiDAR data, and the fusion of these two data sources for forest biomass estimation in the last decade is provided. ...
A summary of previous research on LiDAR-hyperspectral fusion for forest biomass estimation is valuable for further improvement of biomass estimation methods. ...
and 2012BAH27B05). ...
doi:10.1117/1.jrs.8.081598
fatcat:hdvyjfwl3jbrflvrk5lko2xj4u
A new framework for hyperspectral image classification using multiple semisupervised collaborative classification algorithm
2019
IEEE Access
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They are named the syncretic one-fold secondary screening algorithm and semisupervised learning framework (OFSS-SL) and syncretic multiple secondary screening algorithms and multipleverification semisupervised ...
We evaluate the performance of OFSS-SL and MSS-MVSL on three hyperspectral data sets and compare them with that of three state-of-theart classification methods. ...
Combining all the classification results of multiple classifiers determines the final classification results.
2) DISCRIMINATIVE INFORMATION EXCAVATION AND MULTIPLE VERIFICATION In the above-mentioned ...
doi:10.1109/access.2019.2933589
fatcat:2x6lmaxhovh7rbqg4ybilsykcy
A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation
2014
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Spatial features are extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. ...
The proposed framework is validated experimentally on a real dataset acquired in an urban area. ...
ACKNOWLEDGMENTS The authors would like to thank the Hyperspectral Image Analysis group and the NSF Funded Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the datasets ...
doi:10.1109/jstars.2013.2292032
fatcat:coww2srvzbek3kyshswojm7l3u
Multiple composite kernel learning for hyperspectral image classification
2017
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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In this work, we develop a new framework to combine ensemble learning and composite kernel learning for hyperspectral image classification. ...
Then, the new spectral and spatial features are integrated into the composite kernels based on support vector machines classifier. ...
MULTIPLE COMPOSITE KERNEL LEARNING The proposed MCK framework is based on the rotation-based ensemble that aims at generating diverse individual CK classifiers using random feature selection and data transformation ...
doi:10.1109/igarss.2017.8127430
dblp:conf/igarss/DuXGIB17
fatcat:pkumnap4sbhuhozrqlox74olme
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