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  • 1
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    Learning to Understand Remote Sensing Images (2021)
    MDPI - Multidisciplinary Digital Publishing Institute
    Details
    Publication Date: 2023-12-20
    Description: 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: QA75.5-76.95 ; T58.5-58.64 ; metadata ; image classification ; sensitivity analysis ; ROI detection ; residual learning ; image alignment ; adaptive convolutional kernels ; Hough transform ; class imbalance ; land surface temperature ; inundation mapping ; multiscale representation ; object-based ; convolutional neural networks ; scene classification ; morphological profiles ; hyperedge weight estimation ; hyperparameter sparse representation ; semantic segmentation ; vehicle classification ; flood ; Landsat imagery ; target detection ; multi-sensor ; building damage detection ; optimized kernel minimum noise fraction (OKMNF) ; sea-land segmentation ; nonlinear classification ; land use ; SAR imagery ; anti-noise transfer network ; sub-pixel change detection ; Radon transform ; segmentation ; remote sensing image retrieval ; TensorFlow ; convolutional neural network ; particle swarm optimization ; optical sensors ; machine learning ; mixed pixel ; optical remotely sensed images ; object-based image analysis ; very high resolution images ; single stream optimization ; ship detection ; ice concentration ; online learning ; manifold ranking ; dictionary learning ; urban surface water extraction ; saliency detection ; spatial attraction model (SAM) ; quality assessment ; Fuzzy-GA decision making system ; land cover change ; multi-view canonical correlation analysis ensemble ; land cover ; semantic labeling ; sparse representation ; dimensionality expansion ; speckle filters ; hyperspectral imagery ; fully convolutional network ; infrared image ; Siamese neural network ; Random Forests (RF) ; feature matching ; color matching ; geostationary satellite remote sensing image ; change feature analysis ; road detection ; deep learning ; aerial images ; image segmentation ; aerial image ; multi-sensor image matching ; HJ-1A/B CCD ; endmember extraction ; high resolution ; multi-scale clustering ; heterogeneous domain adaptation ; hard classification ; regional land cover ; hypergraph learning ; automatic cluster number determination ; dilated convolution ; MSER ; semi-supervised learning ; gate ; Synthetic Aperture Radar (SAR) ; downscaling ; conditional random fields ; urban heat island ; hyperspectral image ; remote sensing image correction ; skip connection ; ISPRS ; spatial distribution ; geo-referencing ; Support Vector Machine (SVM) ; very high resolution (VHR) satellite image ; classification ; ensemble learning ; synthetic aperture radar ; conservation ; convolutional neural network (CNN) ; THEOS ; visible light and infrared integrated camera ; vehicle localization ; structured sparsity ; texture analysis ; DSFATN ; CNN ; image registration ; UAV ; unsupervised classification ; SVMs ; SAR image ; fuzzy neural network ; dimensionality reduction ; GeoEye-1 ; feature extraction ; sub-pixel ; energy distribution optimizing ; saliency analysis ; deep convolutional neural networks ; sparse and low-rank graph ; hyperspectral remote sensing ; tensor low-rank approximation ; optimal transport ; SELF ; spatiotemporal context learning ; Modest AdaBoost ; topic modelling ; multi-seasonal ; Segment-Tree Filtering ; locality information ; GF-4 PMS ; image fusion ; wavelet transform ; hashing ; machine learning techniques ; satellite images ; climate change ; road segmentation ; remote sensing ; tensor sparse decomposition ; Convolutional Neural Network (CNN) ; multi-task learning ; deep salient feature ; speckle ; canonical correlation weighted voting ; fully convolutional network (FCN) ; despeckling ; multispectral imagery ; ratio images ; linear spectral unmixing ; hyperspectral image classification ; multispectral images ; high resolution image ; multi-objective ; convolution neural network ; transfer learning ; 1-dimensional (1-D) ; threshold stability ; Landsat ; kernel method ; phase congruency ; subpixel mapping (SPM) ; tensor ; MODIS ; GSHHG database ; compressive sensing ; bic Book Industry Communication::U Computing & information technology::UY Computer science
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  • 2
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    Learning to Understand Remote Sensing Images (2021)
    MDPI - Multidisciplinary Digital Publishing Institute
    Details
    Publication Date: 2024-04-14
    Description: 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: QA75.5-76.95 ; T58.5-58.64 ; metadata ; image classification ; sensitivity analysis ; ROI detection ; residual learning ; image alignment ; adaptive convolutional kernels ; Hough transform ; class imbalance ; land surface temperature ; inundation mapping ; multiscale representation ; object-based ; convolutional neural networks ; scene classification ; morphological profiles ; hyperedge weight estimation ; hyperparameter sparse representation ; semantic segmentation ; vehicle classification ; flood ; Landsat imagery ; target detection ; multi-sensor ; building damage detection ; optimized kernel minimum noise fraction (OKMNF) ; sea-land segmentation ; nonlinear classification ; land use ; SAR imagery ; anti-noise transfer network ; sub-pixel change detection ; Radon transform ; segmentation ; remote sensing image retrieval ; TensorFlow ; convolutional neural network ; particle swarm optimization ; optical sensors ; machine learning ; mixed pixel ; optical remotely sensed images ; object-based image analysis ; very high resolution images ; single stream optimization ; ship detection ; ice concentration ; online learning ; manifold ranking ; dictionary learning ; urban surface water extraction ; saliency detection ; spatial attraction model (SAM) ; quality assessment ; Fuzzy-GA decision making system ; land cover change ; multi-view canonical correlation analysis ensemble ; land cover ; semantic labeling ; sparse representation ; dimensionality expansion ; speckle filters ; hyperspectral imagery ; fully convolutional network ; infrared image ; Siamese neural network ; Random Forests (RF) ; feature matching ; color matching ; geostationary satellite remote sensing image ; change feature analysis ; road detection ; deep learning ; aerial images ; image segmentation ; aerial image ; multi-sensor image matching ; HJ-1A/B CCD ; endmember extraction ; high resolution ; multi-scale clustering ; heterogeneous domain adaptation ; hard classification ; regional land cover ; hypergraph learning ; automatic cluster number determination ; dilated convolution ; MSER ; semi-supervised learning ; gate ; Synthetic Aperture Radar (SAR) ; downscaling ; conditional random fields ; urban heat island ; hyperspectral image ; remote sensing image correction ; skip connection ; ISPRS ; spatial distribution ; geo-referencing ; Support Vector Machine (SVM) ; very high resolution (VHR) satellite image ; classification ; ensemble learning ; synthetic aperture radar ; conservation ; convolutional neural network (CNN) ; THEOS ; visible light and infrared integrated camera ; vehicle localization ; structured sparsity ; texture analysis ; DSFATN ; CNN ; image registration ; UAV ; unsupervised classification ; SVMs ; SAR image ; fuzzy neural network ; dimensionality reduction ; GeoEye-1 ; feature extraction ; sub-pixel ; energy distribution optimizing ; saliency analysis ; deep convolutional neural networks ; sparse and low-rank graph ; hyperspectral remote sensing ; tensor low-rank approximation ; optimal transport ; SELF ; spatiotemporal context learning ; Modest AdaBoost ; topic modelling ; multi-seasonal ; Segment-Tree Filtering ; locality information ; GF-4 PMS ; image fusion ; wavelet transform ; hashing ; machine learning techniques ; satellite images ; climate change ; road segmentation ; remote sensing ; tensor sparse decomposition ; Convolutional Neural Network (CNN) ; multi-task learning ; deep salient feature ; speckle ; canonical correlation weighted voting ; fully convolutional network (FCN) ; despeckling ; multispectral imagery ; ratio images ; linear spectral unmixing ; hyperspectral image classification ; multispectral images ; high resolution image ; multi-objective ; convolution neural network ; transfer learning ; 1-dimensional (1-D) ; threshold stability ; Landsat ; kernel method ; phase congruency ; subpixel mapping (SPM) ; tensor ; MODIS ; GSHHG database ; compressive sensing ; thema EDItEUR::U Computing and Information Technology::UY Computer science
    Language: English
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  • 3
    Electronic Resource
    Electronic Resource
    Falling film evaporation of binary mixtures (1994)
    Hoboken, NJ : Wiley-Blackwell
    AIChE Journal 40 (1994), S. 207-214 
    Details
    ISSN: 0001-1541
    Keywords: Chemistry ; Chemical Engineering
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: The interactive effects of heat and mass transfer in evaporation of binary mixtures flowing as falling films on vertical surfaces was investigated. Evaporative heat-transfer coefficients were measured for aqueous mixtures of ethylene and propylene glycol, with boiling ranges up to 55°C. Tests were carried out at atmospheric pressure with heat fluxes ranging from 3,000 to 25,000 W/m2, and film Reynolds numbers ranging from 300 to 3,000. Results indicated that the heat-transfer coefficient for mixtures depends weakly on wall superheat and film Reynolds number, but strongly depends on mixture composition. Analysis of the results indicates that mass-transfer resistance in the liquid film causes significant elevation of the interface temperature, causing a reduction of the effective temperature driving force. A semiempirical model for correlation of the interactive heat-and mass-transfer phenomena is proposed.
    Additional Material: 10 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/aic.690400203
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  • 4
    Electronic Resource
    Electronic Resource
    Catalytic grafting: A new technique for polymer/fiber composites. II. Plasma treated UHMPE fibers/polyethylene composites (1992)
    New York, NY [u.a.] : Wiley-Blackwell
    Journal of Applied Polymer Science 45 (1992), S. 1023-1033 
    Details
    ISSN: 0021-8995
    Keywords: Chemistry ; Polymer and Materials Science
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Notes: In this paper, the catalytic grafting technique for preparation of polymer/fiber composites is extended to plasma treated ultra-high modulus polyethylene (UHMPE) fiber/high density polyethylene (HDPE) system. The OH groups introduced on the UHMPE fiber surface by oxygen plasma treatment were used to chemically anchor Ziegler-Natta catalyst which then was followed by ethylene polymerization on the fiber surface. The morphology and interfacial behavior, as well as the mechanical properties, of the HDPE composites reinforced by catalytic grafted or ungrafted UHMPE fibers were investigated by SEM, DSC, polarized light optical microscopy, and tensile testing. The experimental results show that the polyethylene grafted on the fibers acted as a transition layer between the reinforcing UHMPE fibers and a commercial HDPE matrix. The interfacial adhesion was also significantly improved. Compared with the composite reinforced by ungrafted UHMPE fibers, the composite reinforced by catalytic grafted UHMPE fibers exhibits much better mechanical properties.
    Additional Material: 11 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/app.1992.070450611
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  • 5
    Electronic Resource
    Electronic Resource
    Catalytic grafting: A new technique for polymer-fiber composites I. Polyethylene-asbestos composites (1992)
    New York, NY [u.a.] : Wiley-Blackwell
    Journal of Applied Polymer Science 44 (1992), S. 1107-1119 
    Details
    ISSN: 0021-8995
    Keywords: Chemistry ; Polymer and Materials Science
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Notes: In this work we present a new technique to prepare polyolefin-fiber composites. This technique is based on chemical anchoring of a catalyst on reinforcing agents containing OH groups on their surface and then conducting an olefin polymerization on the supported catalyst. This technique offers the possibility to approach the challenging problems encountered in polymer composites, namely, the reinforcement-matrix adhesion, the dispersion, and the wetting of the reinforcement by the resin. As a first part of a systematic research, we report on the procedure of fixation of titanium tetrachloride on the surface of asbestos fibers and the Ziegler-Natta polymerization of ethylene on the surface-modified fibers. The procedure as well as the structure and properties of the composite were investigated by means of FTIR, atomic absorption, SEM, solvent extraction, and tensile testing. The experimental results show that the Ziegler-Natta catalyst can be efficiently anchored on the surface of the fibers to conduct successful polymerization and to “synthesize” a new class of polymer composites.
    Additional Material: 10 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/app.1992.070440620
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  • 6
    Electronic Resource
    Electronic Resource
    A study on the tribological behavior of polyethylene. II. Effects of operating conditions on the friction behavior of polyethylene (1995)
    New York, NY [u.a.] : Wiley-Blackwell
    Journal of Applied Polymer Science 58 (1995), S. 903-910 
    Details
    ISSN: 0021-8995
    Keywords: Chemistry ; Polymer and Materials Science
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Notes: In this article, the effects of the operating conditions, i.e., load, oscillation speed, temperature, and contact modes on the friction behavior of polyethylene were studied through the SRV vibration friction test machine, the MHK-500 friction and wear test machine, as well as an on-line temperature testing device. The experimental results showed that the friction coefficient μ of polyethylene increases with increase of the oscillation frequency and amplitude, the speed, and PV value, while load has a quite complex impact on μ; suitable choice of load could reduce μ and smoothen the friction process. Contact modes of friction pairs have considerable effects on μ, because all the real contact area of the friction assembly, the pressure, the indentation of surface asperities, as well as the temperature rise and distribution in the contact region are related to contact modes. Temperature is a key factor determining the viscoelastic properties of polyethylene, and therefore has great effect on μ. On-line temperature testing offers a way to reveal the relations between temperature and the friction behavior of polyethylene. All the results obtained provide the basic data for establishing mathematical models and computational simulation methods to describe and study the tribological behavior of some polymer materials. © 1995 John Wiley & Sons, Inc.
    Additional Material: 11 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/app.1995.070580507
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  • 7
    Electronic Resource
    Electronic Resource
    Catalytic grafting: A new technique for polymer-fiber composites. III. Polyethylene-plasma-treated KevlarTM fibers composites: Analysis of the fiber surface (1993)
    New York, NY [u.a.] : Wiley-Blackwell
    Journal of Applied Polymer Science 48 (1993), S. 121-136 
    Details
    ISSN: 0021-8995
    Keywords: Chemistry ; Polymer and Materials Science
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Notes: In this article, plasma-treated KevlarTM fiber-polyethylene composites prepared by the catalytic grafting technique were studied. The reactive groups —COOH, —OH, —NH2 generated on the Kevlar fiber surface by oxygen plasma treatment were used to chemically anchor Ziegler-Natta catalyst, which was then followed by ethylene polymerization on the fiber surface. The surface structure of the Kevlar fibers, untreated or treated by oxygen plasma, catalyst grafted, or ethylene polymerized, was characterized by X-ray photoelectron spectroscopy (XPS), attenuated total reflection (ATR), and scanning electron microscopy (SEM). The morphology, interfacial behavior, and mechanical properties of the high-density polyethylene (HDPE) composites reinforced by either catalytic grafted or ungrafted Kevlar fibers were investigated by means of differential scanning calorimetry (DSC), polarized light optical microscopy, tensile testing, and SEM. Special attention was devoted to the tensile properties of the composites in the direction transverse to the fibers. The experimental results show that oxygen plasma treatment increases the reactive site concentration on the fiber surface significantly and that the composites reinforced by catalytically grafted Kevlar fibers exhibit higher tensile strength both parallel and transverse to the fibers. The improved interfacial adhesion is attributed to the interfacial chemical bonding established by catalytic grafting. © 1993 John Wiley & Sons, Inc.
    Additional Material: 12 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/app.1993.070480113
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  • 8
    Electronic Resource
    Electronic Resource
    Influence of mixed aluminoxane systems on ethylenenorbornene copolymerization catalyzed by metallocene (1997)
    Weinheim : Wiley-Blackwell
    Macromolecular Rapid Communications 18 (1997), S. 1101-1107 
    Details
    ISSN: 1022-1336
    Keywords: Chemistry ; Polymer and Materials Science
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Physics
    Notes: The norbornene/ethylene copolymerization was investigated using Me2SiCp2MCl2 (M = Zr, Ti)/EBAO and MAO as catalyst systems (EBAO: mixed ethyl-isobutylaluminoxane, MAO: methylaluminoxane). The copolymers were characterized by DSC and 13C NMR. Copolymers with high content of norbornene and high Tg were obtained with the mixed EBAO. It is suggested that the copolymerization is greatly influenced by the state of the ion pair of the metallocene catalyst. The effect of aluminoxane on the composition and the microstructure of copolymer is discussed.
    Additional Material: 5 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/marc.1997.030181214
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  • 9
    Electronic Resource
    Electronic Resource
    Interactions between titanium oxide and polystyrene during the pan-milling process (1996)
    New York, NY [u.a.] : Wiley-Blackwell
    Polymer International 41 (1996), S. 245-249 
    Details
    ISSN: 0959-8103
    Keywords: pan-mill type equipment ; PS/TiO2 ; heat expansion coefficient ; polymer stress reaction ; Chemistry ; Polymer and Materials Science
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Notes: The interactions between titanium oxide (TiO2) and polystyrene (PS) during the pan-milling process were studied. The results show that TiO2 contributes to the crushing of PS when they are ground together. Compared with material prepared by the traditional method, the PS/TiO2 prepared by panmilling exhibits much better properties, such as impact strength, rheological behaviour and thermostability. It is a new method for preparing polymer material with high performance.
    Additional Material: 8 Ill.
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  • 10
    Electronic Resource
    Electronic Resource
    Highly Regioselective Nitration of Hexamethylbenzene and Its Derivatives to Dinitroprehnitene with Excess Nitronium TetrafluoroborateAromatic substitutions, Part 61. Part 60:[1] (1990)
    New York, NY : Wiley-Blackwell
    Helvetica Chimica Acta 73 (1990), S. 1167-1170 
    Details
    ISSN: 0018-019X
    Keywords: Chemistry ; Organic Chemistry
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Hexamethylbenzene and its derivatives undergo very clean regioselective dinitration to dinitroprehnitene (1, 2, 3, 4-tetramethyl-5, 6-dinitrobenzene) with excess of nitronium tetrafluoroborate in dry CH2C12 solution. The mechanism of this unexpected new nitration is also discussed.
    Additional Material: 1 Tab.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/hlca.19900730504
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