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Adopting Circular Economy Current Practices and Future Perspectives

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ISBN: 9783039283422 9783039283439 Year: Pages: 160 DOI: 10.3390/books978-3-03928-343-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: General and Civil Engineering --- Technology (General)
Added to DOAB on : 2020-04-07 23:07:08
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The development of a closed-loop cycle is a necessary condition so as to develop a circular economy model as an alternative to the linear model, in order to maintain the value of products and materials for as long as possible. For this motive, the definition of the value must be demonstrated for both the environment and the economy. The presence of these analyses should be associated with the social dimension and the human component. A strong cooperation between social and technical profiles is a new challenge for all researchers. End of life of products attract a lot of attention, and the final output could be the production of technologies suitable for managing this waste.

Satellite Altimetry for Earth Sciences

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ISBN: 9783038976806 9783038976813 Year: Pages: 484 DOI: 10.3390/books978-3-03897-681-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Physics (General) --- Geophysics and Geomagnetism
Added to DOAB on : 2019-08-28 11:21:27
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Satellite altimetry is a radar technique for measuring the topography of the Earth’s surface. It was initially designed for measuring the ocean’s topography, with reference to an ellipsoid, and for the determination of the marine geoid. Satellite altimetry has provided extremely valuable information on ocean science (e.g., circulation surface geostrophic currents, eddy structures, wave heights, and the propagation of oceanic Kelvin and Rossby waves). With more than 25 years of observations, it is also becoming vital to climate research, providing accurate measurements of sea level variations from regional to global scales. Altimetry has also demonstrated a strong potential for geophysical, cryospheric, and hydrological research and is now commonly used for the monitoring of Arctic and Antarctic ice sheet topography and of terrestrial surface water levels. This book aims to present reviews and recent advances of general interest in the use of radar altimetry in Earth sciences. Manuscripts are related to any aspect of radar altimetry technique or geophysical applications. We also encourage manuscripts resulting from the application of new altimetric technology (SAR, SARin, and Ka band) and improvements expected from missions to be launched in the near future (i.e., SWOT).

Keywords

satellite altimetry --- Envisat --- SARAL --- unsupervised classification --- K-medoids --- Greenland Sea --- Fram Strait --- upper layer thickness --- satellite altimeter --- two-layer ocean model --- South China Sea --- coastal altimetry --- sea surface height --- Jason-2 --- waveform retracking --- satellite altimetry --- inland water --- CryosSat-2 SAR --- Mekong Basin --- water level time series --- classification --- stack data --- altimetry --- Ka-band --- data processing --- calibration --- validation --- altimetry --- SAR --- calibration --- validation --- sea surface height --- coastal altimetry --- validation --- tide gauge --- altimetry --- Ka-band --- oceanography --- hydrology --- ice --- geodesy --- Jason-2 --- Hong Kong coast --- retracking --- X-TRACK --- ALES --- PISTACH --- radar altimetry --- coastal altimetry --- sea surface height --- topography of the intertidal zone --- ERS-2 --- ENVISAT --- SARAL --- CryoSat-2 --- altimetry --- water level --- discharge --- Sentinel-3 --- satellite altimetry --- microwave radiometer --- wet tropospheric correction --- wet path delay --- sensor calibration --- HY-2A --- waveform retracking --- range precision --- marine gravity --- radar altimetry --- waveform --- dielectric permittivity --- soil moisture --- satellite altimetry --- SWOT --- western Mediterranean Sea --- fine scale --- SWOT simulator --- ROMS model --- filtering --- storm surge --- satellite altimetry --- calibration --- numerical modelling --- FVCOM --- ocean tides --- coastal altimetry --- ALES retracker --- ocean geostrophy --- water volume transport --- satellite geodesy --- space gravity --- altimetry --- Argo --- Southern Ocean --- ACC --- lake level --- lake volume --- evaporation --- streamflow --- Gravity Recovery and Climate Experiment (GRACE) --- altimetry --- Landsat --- Aral Sea --- altimetry --- water levels --- validation --- Inner Niger Delta --- altimetry --- orbit decay --- drifting orbit --- geodetic orbit --- leads --- satellite altimetry --- CryoSat-2 --- classification --- peakiness --- polar ocean

Learning to Understand Remote Sensing Images

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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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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

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