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Hyperspectral Data Processing Algorithms [chapter]

Antonio Plaza, Javier Plaza, Gabriel Martín, Sergio Sánchez
2018 Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation  
, image coding, or spectral mixture analysis [4] .  ...  Hyperspectral imaging is concerned with the measurement, analysis, and interpretation of spectra acquired from a given scene (or specific object) at a short, medium, or long distance by an airborne or  ...  The authors thank Andreas Mueller for his lead of the DLR project that allowed us to obtain the DAIS 7915 and ROSIS hyperspectral datasets over Dehesa areas in Extremadura, Spain; David Landgrebe at Purdue  ... 
doi:10.1201/9781315164151-11 fatcat:gm3twsueujh3jla6wzuo2piamq

Nonlinear Estimation of Hyperspectral Mixture Pixel Proportion Based on Kernel Orthogonal Subspace Projection [chapter]

Bo Wu, Liangpei Zhang, Pingxiang Li, Jinmu Zhang
2006 Lecture Notes in Computer Science  
Experiments using synthetic data degraded by an AVIRIS image have been carried out, and the results demonstrate that the proposed method can provide excellent proportion estimation for hyperspectral images  ...  A kernel orthogonal subspace projection (KOSP) algorithm has been developed for nonlinear approximating subpixel proportion in this paper.  ...  Bands 35, 24, and 5 are displayed as RGB. 2 Linear Mixture Model Analysis Let r be an 1 × L column image pixel vector in an multispectral or hyperspectral image where L is the number of spectral bands  ... 
doi:10.1007/11759966_157 fatcat:zq6p6d44wjamrbvkyrolckumni

Machine learning based hyperspectral image analysis: A survey [article]

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
2019 arXiv   pre-print
This paper reviews and compares recent machine learning-based hyperspectral image analysis methods published in literature.  ...  Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis.  ...  Machine learning for hyperspectral analysis In this section, we survey recently published machine learning-based hyperspectral image analysis methods.  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

Advanced Processing of Hyperspectral Images

A. Plaza, J.A. Benediktsson, J.W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P.E. Gamba, A. Gaultieri, J.C. Tilton
2006 2006 IEEE International Symposium on Geoscience and Remote Sensing  
In this paper, we provide a seminal view on recent advances in techniques for hyperspectral data processing.  ...  Hyperspectral imaging offers the possibility of characterizing materials and objects in the air, land and water on the basis of the unique reflectance patterns that result from the interaction of solar  ...  Spectral mixture analysis Spectral mixture analysis (or unmixing) has been an alluring exploitation goal since the earliest days of imaging spectroscopy.  ... 
doi:10.1109/igarss.2006.511 dblp:conf/igarss/PlazaBBBBCCFGGTT06 fatcat:mq2bfupfx5cyxbzyjeqnybpwli

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Jose M. Bioucas-Dias, Antonio Plaza, Gustavo Camps-Valls, Paul Scheunders, Nasser Nasrabadi, Jocelyn Chanussot
2013 IEEE Geoscience and Remote Sensing Magazine  
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  ...  This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection  ...  On the other hand, kernel-based methods can design flexible kernels to handle the problem of intimate mixtures.  ... 
doi:10.1109/mgrs.2013.2244672 fatcat:4tk7q6izd5hevhnrck36i5wkiy

Unmixing prior to supervised classification of urban hyperspectral images

Inmaculada Dopido, Antonio Plaza
2011 2011 Joint Urban Remote Sensing Event  
In this paper, we propose a new strategy for feature extraction prior to supervised classification of urban hyperspectral data which is based on spectral unmixing concepts.  ...  Supervised classification of urban hyperspectral images is a very challenging task due to the generally unfavorable ratio between the number of spectral bands and the number of training samples available  ...  In both cases, spectral unmixing provides additional information for classification in urban hyperspectral analysis scenarios, since the sub-pixel composition of training samples can be used as part of  ... 
doi:10.1109/jurse.2011.5764728 dblp:conf/jurse/DopidoP11 fatcat:gdgghj3cgje7zm7knbg42bmlmm

Hyperspectral image classification based on spectral and geometrical features

Bin Luo, Jocelyn Chanussot
2009 2009 IEEE International Workshop on Machine Learning for Signal Processing  
In this paper, we propose to integrate geometrical features, such as the characteristic scales of structures, with spectral features for the classification of hyperspectral images.  ...  Moreover, since the dimension of a hyperspectral image is usually very high, we use linear unmixing algorithm to extract the endmemebers and their abundance maps in order to represent compactly the spectral  ...  Acknowledgement The authors would like to thank Pro Paolo Gamba, University of Pavia, for providing the data and the ground truth of the classification.  ... 
doi:10.1109/mlsp.2009.5306266 fatcat:ktvwy2eq2ndvvlgwebjrjajr6u

Detection of endogenous foreign bodies in Chinese hickory nuts by hyperspectral spectral imaging at the pixel level

Zhe Feng, 1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China, Weihao Li, Di Cui, 2. Key Laboratory of On Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
2022 International Journal of Agricultural and Biological Engineering  
Moreover, the shells in mixtures of shells and kernels were detected based on the proposed deep learning method and visualized for subsequent operations for the removal of foreign bodies.  ...  In this study, a deep learning approach based on a two-dimensional convolutional neural network (2D CNN) and long short-term memory (LSTM) integrated with hyperspectral imaging for distinguishing the shells  ...  [6] acquired hyperspectral images of shells and kernels of walnuts under UV fluorescent lamps at 365 nm and classified walnut shells and kernels using principal component analysis and a Gaussian mixture  ... 
doi:10.25165/j.ijabe.20221502.6881 fatcat:fm3ryh7skzderkg6k7h2pbehwi

Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture/Nonlinear-Fluctuation Model

Jie Chen, Cédric Richard, Paul Honeine
2013 IEEE Transactions on Signal Processing  
Index Terms-Hyperspectral imaging, multi-kernel learning, nonlinear spectral unmixing, support vector regression.  ...  In this paper, we formulate a new kernel-based paradigm that relies on the assumption that the mixing mechanism can be described by a linear mixture of endmember spectra, with additive nonlinear fluctuations  ...  Kernel-based methods have already been considered for detection and classification in hyperspectral images [28] , [29] .  ... 
doi:10.1109/tsp.2012.2222390 fatcat:m35fa2xisrcm5o42zhwm4bk7j4

Unsupervised clustering and spectral unmixing for feature extraction prior to supervised classification of hyperspectral images

Inmaculada Dópido, Alberto Villa, Antonio Plaza
2011 Satellite Data Compression, Communications, and Processing VII  
Classification and spectral unmixing are two very important tasks for hyperspectral data exploitation.  ...  Since hyperspectral images are generally dominated by mixed pixels, spectral unmixing can particularly provide a useful source of information for classification purposes.  ...  problems, 4 which must be necessarily tackled under specific mathematical formalisms, such as classification, segmentation or spectral mixture analysis, of which linear spectral unmixing has been one  ... 
doi:10.1117/12.892469 fatcat:ujhvwhap5fetpfhq76mf4xmu7m

Hyperspectral Image Classification by Fusion of Multiple Classifiers

Yanbin Peng, Zhigang Pan, Zhijun Zheng, Xiaoyong Li
2016 International Journal of Database Theory and Application  
Firstly, to solve the problem of computational complexity, spectral clustering algorithm is imported to select efficient bands for subsequent classification task.  ...  Hyperspectral image mostly have very large amounts of data which makes the computational cost and subsequent classification task a difficult issue.  ...  Liu [4] present a post processing algorithm for a kernel sparse representation based hyperspectral image classifier, which is based on the integration of spatial and spectral information.  ... 
doi:10.14257/ijdta.2016.9.2.20 fatcat:ymsfuojsqzevbhqh2cew5gfgpa

Research on dimension reduction method for hyperspectral remote sensing image based on global mixture coordination factor analysis

S. Wang, C. Wang
2015 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
remote sensing image data based on the global mixture coordination factor analysis.  ...  In the first place, a linear low dimensional manifold is obtained from the nonlinear and high dimensional hyperspectral image data by mixture factor analysis method.  ...  This paper presents a hyperspectral data dimension reduction method of global mixture coordination factor analysis(GMCFA), first of all, it use a mixture of factor analysis method to obtain a linear low-dimensional  ... 
doi:10.5194/isprsarchives-xl-7-w4-159-2015 fatcat:h4v4xf62kvbatjlliin7zcdlzi

The Performance of Classifiers in the Task of Thematic Processing of Hyperspectral Images

Egor V. Dmitriev, Vladimir V. Kozoderov
2016 Journal of Siberian Federal University Engineering & Technologies  
The performance of the spectral classification methods is analyzed for the problem of hyperspectral remote sensing of soil and vegetation.  ...  The results of classification of hyperspectral airborne images by using the specified above methods and comparative analysis are demonstrated.  ...  Developments of Priority Directions in Science and Technology Complex of Russia on 2014-2020" (Grant Agreement No. 14.575.21.0028, its unique identification number RFMEFI57514X0028), the Russian Fund for  ... 
doi:10.17516/1999-494x-2016-9-7-1001-1011 fatcat:ntmfk7qahbb2xa42ijensj7f7q

A Quantitative and Comparative Assessment of Unmixing-Based Feature Extraction Techniques for Hyperspectral Image Classification

Inmaculada Dopido, Alberto Villa, Antonio Plaza, Paolo Gamba
2012 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Over the last years, many feature extraction techniques have been integrated in processing chains intended for hyperspectral image classification.  ...  Recently, a new strategy for feature extraction prior to classification based on spectral unmixing concepts has been introduced.  ...  Johnson for sharing the hyperspectral data sets used in this work.  ... 
doi:10.1109/jstars.2011.2176721 fatcat:22bjnic6urb33gzlan4wdhosj4

Integration of Hyperspectral Image Classification and Unmixing for Active Learning

Jun Li, Antonio Plaza, Jose M. Bioucas-Dias
2011 2011 International Symposium on Image and Data Fusion  
Spectral unmixing is a growing area in remotely sensed hyperspectral image analysis.  ...  In this work, we propose a new method to perform semi-supervised hyperspectral image classification exploiting the information retrieved with spectral unmixing.  ...  CONCLUSION In this work, we have proposed a new active sampling for hyperspectral image classification which integrates discriminative hyperspectral classification with linear spectral unmixing.  ... 
doi:10.1109/isidf.2011.6024216 fatcat:663vmlq54fhwnmaosfscatvihy
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