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Hyperspectral Data Processing Algorithms
[chapter]
2018
Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation
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, 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]
2006
Lecture Notes in Computer Science
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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]
2019
arXiv
pre-print
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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
2006
2006 IEEE International Symposium on Geoscience and Remote Sensing
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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
2013
IEEE Geoscience and Remote Sensing Magazine
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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 ...
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
2011
2011 Joint Urban Remote Sensing Event
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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
2009
2009 IEEE International Workshop on Machine Learning for Signal Processing
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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
2022
International Journal of Agricultural and Biological Engineering
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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
2013
IEEE Transactions on Signal Processing
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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
2011
Satellite Data Compression, Communications, and Processing VII
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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
2016
International Journal of Database Theory and Application
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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
2015
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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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
2016
Journal of Siberian Federal University Engineering & Technologies
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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
2012
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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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
2011
2011 International Symposium on Image and Data Fusion
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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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