Filters








832 Hits in 3.2 sec

Spectral unmixing of multispectral satellite images with dimensionality expansion using morphological profiles

Sergio Bernabé, Prashanth R. Marpu, Antonio Plaza, Jon A. Benediktsson, Bormin Huang, Antonio J. Plaza, Carole Thiebaut, Chulhee Lee, Yunsong Li, Shen-En Qian
2012 Satellite Data Compression, Communications, and Processing VIII  
The unmixing chain considered in this work comprises a classic endmember extraction algorithm: vertex component analysis (VCA) followed by fully constrained linear spectral unmixing (FCLSU) to estimate  ...  For this purpose, in this work, we experiment with morphological profiles and morphological attribute filters, which allow expanding the dimensionality of the original image and obtaining a detailed signature  ...  In this work, we have used the Kernel Principal Component Analysis (KPCA) for spectral information only and the Extended Multi-Attribute Attribute Profiles (EMAPs) 8 for spectral-spatial information.  ... 
doi:10.1117/12.930418 fatcat:54ifvxmftvcglc3r2jcyq6d3i4

Impact of Vector Ordering Strategies on Morphological Unmixing of Remotely Sensed Hyperspectral Images

Antonio Plaza, Javier Plaza
2010 2010 20th International Conference on Pattern Recognition  
Hyperspectral imaging is a new technique in remote sensing that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth.  ...  In previous work, we have explored the application of morphological operations to integrate both spatial and spectral responses in hyperspectral data analysis.  ...  In hyperspectral imagery, the number of spectral bands usually exceeds the number of pure spectral components, called endmembers in hyperspectral analysis terminology [7] , and the unmixing problem is  ... 
doi:10.1109/icpr.2010.1072 dblp:conf/icpr/PlazaP10 fatcat:ybxb7qw33nadlaklndo7q4wyqi

Survey of geometric and statistical unmixing algorithms for hyperspectral images

Mario Parente, Antonio Plaza
2010 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing  
Index Terms-Spectral mixture analysis, hyperspectral imaging, statistical versus geometric unmixing. 978-1-4244-8907-7/10/$26.00 ©2010 IEEE  ...  Spectral mixture analysis (also called spectral unmixing) has been an alluring exploitation goal since the earliest days of imaging spectroscopy.  ...  A kind of dependent component analysis approach for unmixing is also maximization of non-gaussianity (MaxNG) [42] . Support vector machines have also been recently used for unmixing [43, 44] .  ... 
doi:10.1109/whispers.2010.5594929 dblp:conf/whispers/ParenteP10 fatcat:ybec77vlnvc4zhd4rsfdyirbvi

An overview on hyperspectral unmixing: Geometrical, statistical, and sparse regression based approaches

Jose M. Bioucas-Dias, Antonio Plaza
2011 2011 IEEE International Geoscience and Remote Sensing Symposium  
Spectral unmixing is, thus, a source separation problem. This paper presents an overview of the principal research directions in hyperspectral unmixing.  ...  Given a set of mixed spectral vectors, spectral mixture analysis (or spectral unmixing) aims at estimating the number of reference materials, also called endmembers, their spectral signatures, and their  ...  the Bayesian nonnegative matrix factorization [36] , the dependent component analysis DECA [37] , and the em generalized bilinear model [38] , which adopts a non-linear observation model accounting  ... 
doi:10.1109/igarss.2011.6049397 dblp:conf/igarss/Bioucas-DiasP11 fatcat:4likewven5hkpkmh6de3fmach4

Spectral Characterization and Unmixing of Intrinsic Contrast in Intact Normal and Diseased Gastric Tissues Using Hyperspectral Two-Photon Microscopy

Lauren E. Grosberg, Andrew J. Radosevich, Samuel Asfaha, Timothy C. Wang, Elizabeth M. C. Hillman, Wei-Chun Chin
2011 PLoS ONE  
Our results show that hyperspectral unmixing with excitation spectra allows segmentation, showing promise for blind identification of tissue types within a field of view, analogous to specific staining  ...  our ability to clearly visualize morphology.  ...  Contributed reagents/materials/analysis tools: AJR SA TCW. Wrote the paper: LEG EMCH.  ... 
doi:10.1371/journal.pone.0019925 pmid:21603623 pmcid:PMC3095627 fatcat:ly7ycrmeafgoneljvzr544q5ha

Comparison of support vector machine-based processing chains for hyperspectral image classification

Marta Rojas, Inmaculada Dópido, Antonio Plaza, Paolo Gamba, Bormin Huang, Antonio J. Plaza, Joan Serra-Sagristà, Chulhee Lee, Yunsong Li, Shen-En Qian
2010 Satellite Data Compression, Communications, and Processing VI  
Many different approaches have been proposed in recent years for remotely sensed hyperspectral image classification.  ...  Generally speaking, a hyperspectral image classification chain may be defined from two perspectives: 1) the provider's viewpoint, and 2) the user's viewpoint, where the first part of the chain comprises  ...  Moreover, other techniques such as texture and morphological analysis, or linear spectral unmixing, are included in some chains to obtain best features from different perspectives (spatial information,  ... 
doi:10.1117/12.860413 fatcat:6g4ncfg3fbe3xnfo753zx273km

HyperMix: A new tool for quantitative evaluation of end member identification and spectral unmixing techniques

Luis-Ignacio Jimenez, Gabriel Martin, Antonio Plaza
2012 2012 IEEE International Geoscience and Remote Sensing Symposium  
The tool also includes a database of synthetic hyperspectral images (generated using fractals to simulate natural patterns) which can be used to evaluate the precision of the algorithms for endmember identification  ...  In this paper, we present a new open source system for evaluating and inter-comparing new spectral unmixing applications.  ...  For this purpose, the tool includes an open source implementation of principal component analysis (PCA) [7] . Fig. 1 .Fig. 2 . 12 Standard hyperspectral unmixing chain.  ... 
doi:10.1109/igarss.2012.6351276 dblp:conf/igarss/JimenezMP12 fatcat:22hojk7wznbrloqizuf7vrnfmu

Incorporation of spatial constraints into spectral mixture analysis of remotely sensed hyperspectral data

Antonio Plaza, Javier Plaza, Gabriel Martin
2009 2009 IEEE International Workshop on Machine Learning for Signal Processing  
Spectral mixture analysis is an important technique to analyze remotely sensed hyperspectral data sets.  ...  hyperspectral scenes with artificial spatial patterns generated using fractals, and a real hyperspectral scene collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).  ...  Real hyperspectral data A well-known hyperspectral data set has been selected for the purpose of illustrating the spectral unmixing algorithm described in this work.  ... 
doi:10.1109/mlsp.2009.5306202 fatcat:sb5mei44cjgiraggrdquuk767u

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.  ...  terms of variance or signal-to-noise ratio (SNR) as it is the case with other transformations such as principal component analysis (PCA) or the minimum noise fraction (MNF).  ...  Techniques used for this purpose include principal component analysis (PCA) [4] or the minimum noise transform (MNF) [14] .  ... 
doi:10.1109/jurse.2011.5764728 dblp:conf/jurse/DopidoP11 fatcat:gdgghj3cgje7zm7knbg42bmlmm

Fuzzy Based Hyperspectral Image Segmentation Using Subpixel Detection[

Veera SenthilKumar.G, Dhivya. M, Sivasangari. R, Suganya. V
2014 International Journal of Information Sciences and Techniques  
Principal Component Analysis (PCA) is the basic step adopted to reduce the high dimensional data of image to low dimensional data.  ...  Mixture tuned matched filtering technique is used for sub pixel target detection because it is a combination of linear spectral unmixing and matched filtering and has advantages of both the techniques.  ...  For this reason, there is great demand in developing fast detection techniques in hyperspectral imaging for applications such as Aerial surveillance, agricultural and chemical analysis, mineral analysis  ... 
doi:10.5121/ijist.2014.4322 fatcat:emizzvytrrg2hpvzllqwo3itam

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

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
2019 arXiv   pre-print
Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis.  ...  The image analysis tasks considered are land cover classification, target detection, unmixing, and physical parameter estimation.  ...  [324] applied rotation forest for hyperspectral data, with rotational transformations generated by principal component analysis (PCA), minimum noise fraction (MNF), independent component analysis (ICA  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

Parallel Hyperspectral Image and Signal Processing [Applications Corner]

Antonio Plaza, Javier Plaza, Abel Paz, Sergio Sanchez
2011 IEEE Signal Processing Magazine  
SPECTRAL UNMIXING OF HYPERSPECTRAL DATA Spectral unmixing involves the separation of a pixel spectrum into its pure component endmember spectra and the estimation of the abundance value for each endmember  ...  Through the analysis of a standard hyperspectral unmixing chain, we have illustrated different parallel systems and strategies to increase computational performance of hyperspectral imaging algorithms.  ... 
doi:10.1109/msp.2011.940409 fatcat:cfi25inyprfyxibtrozh36w3km

Unmixing Prior to Supervised Classification of Remotely Sensed Hyperspectral Images

Inmaculada Dopido, Maciel Zortea, Alberto Villa, Antonio Plaza, Paolo Gamba
2011 IEEE Geoscience and Remote Sensing Letters  
In this letter, we explore the use of spectral unmixing for feature extraction prior to supervised classification of hyperspectral data using support vector machines.  ...  Supervised classification of 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 a priori  ...  Crawford for sharing the hyperspectral data and the two reviewers for their comments.  ... 
doi:10.1109/lgrs.2011.2109367 fatcat:eoyemhzznjc3pjmnrpiv4pdg3u

Endmember extraction algorithms from hyperspectral images

M. C. Cantero, P. L. Aguilar, A. Plaza, R. M. Pérez, P. J. Martínez, J. Plaza
2006 Annals of Geophysics  
During the last years, several high-resolution sensors have been developed for hyperspectral remote sensing applications. Some of these sensors are already available on space-borne devices.  ...  them to real hyperspectral data.  ...  Pedro Gómez Vilda (Universidad Politécnica de Madrid) for his valuable suggestions and comments on the work described in this paper.  ... 
doi:10.4401/ag-3156 doaj:200221ff074e4872b65f629e32b116b9 fatcat:6xfzpf3phra7pje4mbcyhyu3k4

Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data

Jun Li, Jose M. Bioucas-Dias
2008 IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium  
This paper presents a new method of minimum volume class for hyperspectral unmixing, termed minimum volume simplex analysis (MVSA).  ...  MVSA approaches hyperspectral unmixing by fitting a minimum volume simplex to the hyperspectral data, constraining the abundance fractions to belong to the probability simplex.  ...  The resulting algorithm, termed DECA, for dependent component analysis, implements an expectation maximization iteratative scheme for the inference of the endmember signatures (mixing matrix) and the density  ... 
doi:10.1109/igarss.2008.4779330 dblp:conf/igarss/LiB08a fatcat:afnqb24nwvgghazqhagfu4q4z4
« Previous Showing results 1 — 15 out of 832 results