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Hyperspectral Image Classification by Fusion of Multiple Classifiers

Yanbin Peng, Zhigang Pan, Zhijun Zheng, Xiaoyong Li
2016 International Journal of Database Theory and Application  
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"

Chein-I Chang, Meiping Song, Junping Zhang, Chao-Cheng Wu
2019 Remote Sensing  
, 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

Yi Liu, Jun Li, Antonio Plaza, Jose Bioucas-Dias, Aurora Cuartero, Pablo Garcia Rodriguez
2014 2014 IEEE Geoscience and Remote Sensing Symposium  
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

Bharath Bhushan Damodaran, Rama Rao Nidamanuri, Yuliya Tarabalka
2015 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
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

Shrutika Sawant, Manoharan Prabukumar
2020 Journal of Spectral Imaging  
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

Reddy Pullanagari, Gábor Kereszturi, Ian J. Yule, Pedram Ghamisi
2017 Journal of Applied Remote Sensing  
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

Bharath Bhushan Damodaran, Nicolas Courty, Sebastien Lefevre
2016 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
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

Tatireddy Reddy, Jonnadula Harikiran
2022 Journal of Spectral Imaging  
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

Hongjun Su, Bin Yong, Peijun Du, Hao Liu, Chen Chen, Kui Liu
2014 Journal of Applied Remote Sensing  
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

D. B. Bhushan, R. R. Nidamanuri
2014 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
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

Hui Liu, Chenming Li, Lizhong Xu
2019 Computer Science and Information Systems  
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

Qixia Man, Pinliang Dong, Huadong Guo, Guang Liu, Runhe Shi
2014 Journal of Applied Remote Sensing  
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

Ying Cui, Xiaowei Ji, Heng Wang, Kai Xu, Shaoqiao Wu, Liguo Wang
2019 IEEE Access  
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

Jinha Jung, Edoardo Pasolli, Saurabh Prasad, James C. Tilton, Melba M. Crawford
2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
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

Peijun Du, Junshi Xia, Pedram Ghamisi, Akira Iwasaki, Jon Atli Benediktsson
2017 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
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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