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Materialklassifikation in optischen Inspektionssystemen mithilfe hyperspektraler Daten

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Book Series: Forschungsberichte aus der Industriellen Informationstechnik / Institut für Industrielle Informationstechnik (IIIT), Karlsruher Institut für Technologie ISSN: 21906629 ISBN: 9783731502739 Year: Volume: 10 Pages: XI, 222 p. DOI: 10.5445/KSP/1000043415 Language: GERMAN
Publisher: KIT Scientific Publishing
Subject: Technology (General)
Added to DOAB on : 2019-07-30 20:02:00
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In this work, different methods for material classification by hyperspectral images in the near infrared range are investigated. Specifically, the design of problem-adapted camera systems and the choice of optimal optical filters are discussed. In addition, a method for the fusion of multiple camera signals by spectral unmixing is presented.

The Future of Hyperspectral Imaging

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ISBN: 9783039218226 9783039218233 Year: Pages: 220 DOI: 10.3390/books978-3-03921-823-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Chemistry (General)
Added to DOAB on : 2019-12-09 16:39:37
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Keywords

hyperspectral imaging --- Raman --- fluorescence --- sorting --- quality control --- black polymers --- PZT --- classification --- machine learning --- alternating direction method of multipliers --- Cramer–Rao lower bound --- forward observation model --- linear mixture model --- maximum likelihood --- multiband image fusion --- total variation --- fingerprints --- blood detection --- age determination --- hyperspectral imaging --- lossless compression --- multitemporal hyperspectral images --- information theoretic analysis --- predictive coding --- hyperspectral imaging --- plant phenotyping --- disease detection --- spectral tracking --- time series --- hyperspectral imaging --- principal component analysis --- oxygen saturation --- wound healing --- diabetic foot ulcer --- Raman spectroscopy --- chemical imaging --- compressive detection --- spatial light modulators (SLM) --- digital micromirror device (DMD) --- digital light processor (DLP) --- optimal binary filters --- Chemometrics --- multivariate data analysis --- compressive sensing --- hyperspectral imaging --- multiplexing system --- liquid crystal --- three-dimensional imaging --- integral imaging --- remote sensing --- point target detection --- CS-MUSI --- hyperspectral --- video --- imaging --- coastal dynamics --- moving vehicle imaging --- bi-directional reflectance distribution function (BRDF) --- hemispherical conical reflectance factor (HCRF) --- stereo imaging --- digital elevation model --- Virginia Coast Reserve Long Term Ecological Research (VCR LTER) --- Hyperspectral imaging --- painting samples --- retouching pigments --- watercolours --- multivariate analysis --- potatoes --- sprouting --- primordial leaf count --- hyperspectral imaging --- spectroscopy --- fusion --- wavelength selection --- PLSR --- interval partial least squares --- deep learning --- hyperspectral imaging --- neural networks --- machine learning --- image processing --- hyperspectral imaging --- medical imaging by HSI --- HSI for biology --- remote sensing --- hyperspectral microscopy --- fluorescence hyperspectral imaging --- Raman hyperspectral imaging --- infrared hyperspectral imaging --- statistical methods for HSI --- hyperspectral data mining and compression --- statistical methods for HSI --- hyperspectral data mining and compression

Hyperspectral Image Unmixing Incorporating Adjacency Information

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Book Series: Forschungsberichte aus der Industriellen Informationstechnik / Institut für Industrielle Informationstechnik (IIIT), Karlsruher Institut für Technologie ISSN: 21906629 ISBN: 9783731507888 Year: Volume: 18 Pages: XIII, 203 p. DOI: 10.5445/KSP/1000081665 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Technology (General)
Added to DOAB on : 2019-07-28 18:37:01
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While the spectral information contained in hyperspectral images is rich, the spatial resolution of such images is in many cases very low. Many pixel spectra are mixtures of pure materials’ spectra and therefore need to be decomposed into their constituents. This work investigates new decomposition methods taking into account spectral, spatial and global 3D adjacency information. This allows for faster and more accurate decomposition results.

A Contribution to Active Infrared Laser Spectroscopy for Remote Substance Detection

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Book Series: Schriftenreihe Automatische Sichtprüfung und Bildverarbeitung ISSN: 18665934 ISBN: 9783731507253 Year: Volume: 10 Pages: XIV, 189 p. DOI: 10.5445/KSP/1000074148 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 20:01:59
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In this work we present a stand-off, long wave infrared (LWIR) spectroscopy system for remote detection of hazardous substances. The principle is based upon wavelength selective illumination using External Cavity Quantum Cascade Lasers, that are tunable in the LWIR wavelength range from 7.5 - 10 µm, in which most chemical substances exhibit a highly characteristic absorption behaviour.

Recent Advances in Remote Sensing for Crop Growth Monitoring

ISBN: 9783038422266 9783038422273 Year: Pages: XX, 386 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Added to DOAB on : 2016-08-19 08:32:15
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This special issue book gathers sixteen papers focusing on the application of remote sensing techniques to crop growth monitoring. The studies feature multi-scale and multi-source remotely sensed data, a combination of empirical and physical approaches, and a range of topics on crop growth parameters estimation and crop mapping. It is recommended to graduate students, professors, scientists and engineers who have broad interests in the agricultural applications of remote sensing.

Advancing Earth Surface Representation via Enhanced Use of Earth Observations in Monitoring and Forecasting Applications

Authors: --- --- --- --- et al.
ISBN: 9783039210640 9783039210657 Year: Pages: 262 DOI: 10.3390/books978-3-03921-065-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General)
Added to DOAB on : 2019-12-09 11:49:15
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The representation of the Earth's surface in global monitoring and forecasting applications is moving towards capturing more of the relevant processes, while maintaining elevated computational efficiency and therefore a moderate complexity. These schemes are developed and continuously improved thanks to well instrumented field-sites that can observe coupled processes occurring at the surface–atmosphere interface (e.g., forest, grassland, cropland areas and diverse climate zones). Approaching global kilometer-scale resolutions, in situ observations alone cannot fulfil the modelling needs, and the use of satellite observation becomes essential to guide modelling innovation and to calibrate and validate new parameterization schemes that can support data assimilation applications. In this book, we review some of the recent contributions, highlighting how satellite data are used to inform Earth surface model development (vegetation state and seasonality, soil moisture conditions, surface temperature and turbulent fluxes, land-use change detection, agricultural indicators and irrigation) when moving towards global km-scale resolutions.

Advances in Near Infrared Spectroscopy and Related Computational Methods

Authors: ---
ISBN: 9783039280520 9783039280537 Year: Pages: 496 DOI: 10.3390/books978-3-03928-053-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Chemistry (General) --- Analytical Chemistry
Added to DOAB on : 2020-01-30 16:39:46
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In the last few decades, near-infrared (NIR) spectroscopy has distinguished itself as one of the most rapidly advancing spectroscopic techniques. Mainly known as an analytical tool useful for sample characterization and content quantification, NIR spectroscopy is essential in various other fields, e.g. NIR imaging techniques in biophotonics, medical applications or used for characterization of food products. Its contribution in basic science and physical chemistry should be noted as well, e.g. in exploration of the nature of molecular vibrations or intermolecular interactions. One of the current development trends involves the miniaturization and simplification of instrumentation, creating prospects for the spread of NIR spectrometers at a consumer level in the form of smartphone attachments—a breakthrough not yet accomplished by any other analytical technique. A growing diversity in the related methods and applications has led to a dispersion of these contributions among disparate scientific communities. The aim of this Special Issue was to bring together the communities that may perceive NIR spectroscopy from different perspectives. It resulted in 30 contributions presenting the latest advances in the methodologies essential in near-infrared spectroscopy in a variety of applications.

Keywords

hyperspectral imaging --- variety discrimination --- Chrysanthemum --- deep convolutional neural network --- DNA --- FTIR spectroscopy --- rapid identification --- PLS-DA --- animal origin --- near-infrared hyperspectral imaging --- raisins --- support vector machine --- pixel-wise --- object-wise --- maize kernel --- hyperspectral imaging technology --- accelerated aging --- principal component analysis --- support vector machine model --- standard germination tests --- blackberries --- Rubus fructicosus --- phenolics --- carotenoids --- bioanalytical applications --- near infrared --- chemometrics --- VIS/NIR hyperspectral imaging --- corn seed --- classification --- freeze-damaged --- image processing --- imaging visualization --- wavelength selection --- NIR spectroscopy --- binary dragonfly algorithm --- ensemble learning --- quantitative analysis modeling --- NIR --- SCiO --- pocket-sized spectrometer --- cheese --- fat --- moisture --- multivariate data analysis --- Fourier-transform near-infrared spectroscopy --- glucose --- fructose --- dry matter --- partial least square regression --- Ewing sarcoma --- Fourier transform infrared spectroscopy --- FTIR --- chemotherapy --- bone cancer --- calibration transfer --- NIR spectroscopy --- PLS --- quantitative analysis model --- melamine --- FT-IR --- NIR spectroscopy --- quantum chemical calculation --- anharmonic calculation --- overtones --- combination bands --- near infrared spectroscopy --- Trichosanthis Fructus --- geographical origin --- chemometric techniques --- crude drugs --- prepared slices --- support vector machine-discriminant analysis --- near-infrared fluorescence --- fluorescent probes --- Zn(II) --- di-(2-picolyl)amine --- living cells --- cellular imaging --- near-infrared (NIR) spectroscopy --- calibration transfer --- affine invariance --- multivariate calibration --- partial least squares (PLS) --- NIR --- direct model transferability --- MicroNIR™ --- SVM --- hier-SVM --- SIMCA --- PLS-DA --- TreeBagger --- PLS --- calibration transfer --- agriculture --- photonics --- imaging --- spectral imaging --- spectroscopy --- handheld near-infrared spectroscopy --- pasta/sauce blends --- partial least squares calibration --- nutritional parameters --- bootstrapping soft shrinkage --- partial least squares --- extra virgin olive oil --- adulteration --- FT-NIR spectroscopy --- near-infrared spectroscopy --- ethanol --- anharmonic quantum mechanical calculations --- isotopic substitution --- overtones --- combinations bands --- seeds vitality --- rice seeds --- near-infrared spectroscopy --- hyperspectral image --- discriminant analysis --- near-infrared spectroscopy --- counter propagation artificial neural network --- detection --- auxiliary diagnosis --- BRAF V600E mutation --- colorectal cancer --- tissue --- paraffin-embedded --- deparaffinized --- stained --- ultra-high performance liquid chromatography --- Folin–Ciocalteu --- total hydroxycinnamic derivatives --- phytoextraction --- near-infrared spectroscopy --- origin traceability --- data fusion --- Paris polyphylla var. yunnanensis --- Fourier transform mid-infrared spectroscopy --- near-infrared spectroscopy --- aquaphotomics --- water --- light --- near infrared spectroscopy --- water-mirror approach --- perturbation --- biomeasurements --- biodiagnosis --- biomonitoring --- Vitis vinifera L. --- proximal sensing --- precision viticulture --- near infrared --- chemometrics --- non-destructive sensor --- NIRS --- osteopathy --- late preterm --- brain --- splanchnic --- Raman spectroscopy --- hyperspectral imaging --- analytical spectroscopy --- counterfeit and substandard pharmaceuticals --- DFT calculations --- chemometrics --- PLSR --- API --- lumefantrine --- artemether --- antimalarial tablets --- FT-NIR spectroscopy --- PLS-R --- water --- glucose --- test set validation --- RMSEP --- hyperspectral image processing --- perfusion measurements --- clinical classifications --- n/a

Learning to Understand Remote Sensing Images

Author:
ISBN: 9783038976844 9783038976851 Year: Volume: 1 Pages: 426 DOI: 10.3390/books978-3-03897-685-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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Abstract

With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

Learning to Understand Remote Sensing Images

Author:
ISBN: 9783038976981 9783038976998 Year: Volume: 2 Pages: 376 DOI: 10.3390/books978-3-03897-699-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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Abstract

With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

Drones for Biodiversity Conservation and Ecological Monitoring

Authors: ---
ISBN: 9783039219803 9783039219810 Year: Pages: 176 DOI: 10.3390/books978-3-03921-981-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General)
Added to DOAB on : 2020-01-07 09:08:26
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Abstract

Unmanned aerial vehicles (UAV) have already become an affordable and cost-efficient tool to quickly map a targeted area for many emerging applications in the arena of ecological monitoring and biodiversity conservation. Managers, owners, companies, and scientists are using professional drones equipped with high-resolution visible, multispectral, or thermal cameras to assess the state of ecosystems, the effect of disturbances, or the dynamics and changes within biological communities inter alia. We are now at a tipping point on the use of drones for these type of applications over natural areas. UAV missions are increasing but most of them are testing applicability. It is time now to move to frequent revisiting missions, aiding in the retrieval of important biophysical parameters in ecosystems or mapping species distributions. This Special Issue shows UAV applications contributing to a better understanding of biodiversity and ecosystem status, threats, changes, and trends. It documents the enhancement of knowledge in ecological integrity parameters mapping, long-term ecological monitoring based on drones, mapping of alien species spread and distribution, upscaling ecological variables from drone to satellite images: methods and approaches, rapid risk and disturbance assessment using drones, mapping albedo with UAVs, wildlife tracking, bird colony and chimpanzee nest mapping, habitat mapping and monitoring, and a review on drones for conservation in protected areas.

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