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Remote sensing is being actively researched in the fields of environment, military and urban planning through technologies such as monitoring of natural climate phenomena on the earth, land cover classification, and object detection. Recently, satellites equipped with observation cameras of various resolutions were launched, and remote sensing images are acquired by various observation methods including cluster satellites. However, the atmospheric and environmental conditions present in the observed scene degrade the quality of images or interrupt the capture of the Earth's surface information. One method to overcome this is by generating synthetic images through image simulation. Synthetic images can be generated by using statistical or knowledge-based models or by using spectral and optic-based models to create a simulated image in place of the unobtained image at a required time. Various proposed methodologies will provide economical utility in the generation of image learning materials and time series data through image simulation. The 6 published articles cover various topics and applications central to Remote sensing image simulation. Although submission to this Special Issue is now closed, the need for further in-depth research and development related to image simulation of High-spatial and spectral resolution, sensor fusion and colorization remains.I would like to take this opportunity to express my most profound appreciation to the MDPI Book staff, the editorial team of Applied Sciences journal, especially Ms. Nimo Lang, the assistant editor of this Special Issue, talented authors, and professional reviewers.

image fusion --- random forest regression --- SAR image --- panchromatic image --- high-resolution --- multi-beam LiDAR --- in situ self-calibration --- mobile mapping system --- 3D point cloud --- backpack-based mapping --- aerial orthoimage --- Sentinel-2 --- super-resolution --- image simulation --- residual U-Net --- interferometry --- remote sensing --- computational simulation --- denoising --- detection --- SAR imagery --- fusing region proposals --- KOMPSAT-3A --- strip --- sensor modeling --- RPCs --- mosaic --- matching --- discrepancy --- n/a

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Because of its ability to sense the Earth’s surface at night and during the day, under any weather condition, Synthetic Aperture Radar (SAR) has become a well-established and powerful remote sensing technology that is used worldwide for numerous applications. This book compiles 19 research works that investigate different aspects of SAR processing, SAR image analysis, and SAR applications. The contributions cover topics related to multi-angle/wide-angle SAR imaging; Doppler parameter estimation; data-driven focusing; Inverse SAR (ISAR) applied to pulsar signal modeling and detection; ground-based SAR; near-field interferometric ISAR; the interaction between SAR signals and the Infosphere; SAR interferometry for ground displacement monitoring, feature extraction, and change detection; and SAR-based sea applications. The selected studies represent real examples of the abundant research ongoing in the field of SAR processing and applications, and they further demonstrate that SAR imaging still presents considerable opportunities for future investigation.

Research & information: general --- joint sparse reconstruction --- interferometric inverse synthetic aperture radar --- compressed sensing --- near-field 3-D imaging --- wide angle --- synthetic aperture radar (SAR) --- convolutional neural networks (CNNs) --- deep learning (DL) --- ship classification --- Doppler parameter estimation and compensation (DPEC) --- extended multiple aperture mapdrift (EMAM) --- very-high-squint airborne SAR imaging --- spatial variance --- the derivative of the Doppler rate --- GB-SAR --- MIMO radar --- radar imaging --- CSAR --- anisotropy --- aspect entropy --- discrimination --- wide angle SAR --- LS-CS-Residual --- aspect dependent --- polarimetric synthetic aperture radar --- total electron content --- ionospheric electron density distribution --- land subsidence --- Radarsat-2 images --- small baseline subset (SBAS) method --- interferometric synthetic aperture radar (InSAR) --- inverse synthetic aperture radar (ISAR) --- moving ship --- refocusing --- fast minimum entropy --- oceanic eddies --- shear-wave-generated eddies --- burgers-Rott vortex model --- SAR image simulation --- SAR system --- efficient focusing of SAR data --- inverse problem --- remote sensing --- SAR data-focusing --- synthetic aperture radar --- Singular Value Decomposition --- blind deconvolution --- signal processing --- parameter estimation --- computational modeling --- SAR imaging --- multi-angle SAR --- improved RMA --- SAR image fusion --- wide-area surveillance --- super-resolution --- Doppler beam sharpening --- ionosphere --- P-band --- reverse back-projection (ReBP) --- sliding spotlight --- scintillation --- SAR --- 2CMV --- change detection --- optical flow --- k-means --- K-SVD --- multi-pass squinted (MPS) --- azimuth sidelobes suppression --- ground-based synthetic aperture radar (GBSAR) --- arc-scanning synthetic aperture radar (ArcSAR) --- interferometric ArcSAR --- DEM assisted SAR imaging --- deformation model --- time series deformation --- rheological parameter --- highway --- passive ISAR asteroid imaging --- pulsar emission signal modeling --- multi-angle/wide angle SAR --- inverse SAR --- ground-based SAR --- ionospheric effects --- SAR interferometry --- SAR image analysis --- SAR image change detection --- SAR sea applications --- joint sparse reconstruction --- interferometric inverse synthetic aperture radar --- compressed sensing --- near-field 3-D imaging --- wide angle --- synthetic aperture radar (SAR) --- convolutional neural networks (CNNs) --- deep learning (DL) --- ship classification --- Doppler parameter estimation and compensation (DPEC) --- extended multiple aperture mapdrift (EMAM) --- very-high-squint airborne SAR imaging --- spatial variance --- the derivative of the Doppler rate --- GB-SAR --- MIMO radar --- radar imaging --- CSAR --- anisotropy --- aspect entropy --- discrimination --- wide angle SAR --- LS-CS-Residual --- aspect dependent --- polarimetric synthetic aperture radar --- total electron content --- ionospheric electron density distribution --- land subsidence --- Radarsat-2 images --- small baseline subset (SBAS) method --- interferometric synthetic aperture radar (InSAR) --- inverse synthetic aperture radar (ISAR) --- moving ship --- refocusing --- fast minimum entropy --- oceanic eddies --- shear-wave-generated eddies --- burgers-Rott vortex model --- SAR image simulation --- SAR system --- efficient focusing of SAR data --- inverse problem --- remote sensing --- SAR data-focusing --- synthetic aperture radar --- Singular Value Decomposition --- blind deconvolution --- signal processing --- parameter estimation --- computational modeling --- SAR imaging --- multi-angle SAR --- improved RMA --- SAR image fusion --- wide-area surveillance --- super-resolution --- Doppler beam sharpening --- ionosphere --- P-band --- reverse back-projection (ReBP) --- sliding spotlight --- scintillation --- SAR --- 2CMV --- change detection --- optical flow --- k-means --- K-SVD --- multi-pass squinted (MPS) --- azimuth sidelobes suppression --- ground-based synthetic aperture radar (GBSAR) --- arc-scanning synthetic aperture radar (ArcSAR) --- interferometric ArcSAR --- DEM assisted SAR imaging --- deformation model --- time series deformation --- rheological parameter --- highway --- passive ISAR asteroid imaging --- pulsar emission signal modeling --- multi-angle/wide angle SAR --- inverse SAR --- ground-based SAR --- ionospheric effects --- SAR interferometry --- SAR image analysis --- SAR image change detection --- SAR sea applications

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Remote sensing is being actively researched in the fields of environment, military and urban planning through technologies such as monitoring of natural climate phenomena on the earth, land cover classification, and object detection. Recently, satellites equipped with observation cameras of various resolutions were launched, and remote sensing images are acquired by various observation methods including cluster satellites. However, the atmospheric and environmental conditions present in the observed scene degrade the quality of images or interrupt the capture of the Earth's surface information. One method to overcome this is by generating synthetic images through image simulation. Synthetic images can be generated by using statistical or knowledge-based models or by using spectral and optic-based models to create a simulated image in place of the unobtained image at a required time. Various proposed methodologies will provide economical utility in the generation of image learning materials and time series data through image simulation. The 6 published articles cover various topics and applications central to Remote sensing image simulation. Although submission to this Special Issue is now closed, the need for further in-depth research and development related to image simulation of High-spatial and spectral resolution, sensor fusion and colorization remains.I would like to take this opportunity to express my most profound appreciation to the MDPI Book staff, the editorial team of Applied Sciences journal, especially Ms. Nimo Lang, the assistant editor of this Special Issue, talented authors, and professional reviewers.

Technology: general issues --- History of engineering & technology --- image fusion --- random forest regression --- SAR image --- panchromatic image --- high-resolution --- multi-beam LiDAR --- in situ self-calibration --- mobile mapping system --- 3D point cloud --- backpack-based mapping --- aerial orthoimage --- Sentinel-2 --- super-resolution --- image simulation --- residual U-Net --- interferometry --- remote sensing --- computational simulation --- denoising --- detection --- SAR imagery --- fusing region proposals --- KOMPSAT-3A --- strip --- sensor modeling --- RPCs --- mosaic --- matching --- discrepancy --- n/a

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Remote sensing plays a pivotal role in understanding where and how floods and glacier geohazards occur; their severity, causes and types; and the risk that they may pose to populations, activities and properties. By providing a spectrum of imaging capabilities, resolutions and temporal and spatial coverage, remote sensing data acquired from satellite, aerial and ground-based platforms provide key geo-information to characterize and model these processes. This book includes research papers on novel technologies (e.g., sensors, platforms), data (e.g., multi-spectral, radar, laser scanning, GPS, gravity) and analysis methods (e.g., change detection, offset tracking, structure from motion, 3D modeling, radar interferometry, automated classification, machine learning, spectral indices, probabilistic approaches) for flood and glacier imaging. Through target applications and case studies distributed globally, these articles contribute to the discussion on the current potential and limitations of remote sensing in this specialist research field, as well as the identification of trends and future perspectives.

Research & information: general --- glacier surge --- glacier collapse --- rock-slope instability --- hazard --- Landsat --- Sentinel 2 --- Tibet --- flood extent mapping --- supervised classification --- NDWI --- synthetic aperture radar (SAR) --- web application --- synthetic aperture radar --- offset tracking --- displacements --- Sentinel-1 --- glacier monitoring --- flood mapping --- damage assessment --- SAR image --- Landsat-8 --- Google Earth Engine --- GEE --- Bangladesh --- SAR intensity time series --- urban flood mapping --- double bounce effect --- Hurricane Matthew --- flood --- FPI --- GRACE --- terrestrial water storage anomaly --- storage deficit --- mass balance --- snow depth --- glacier retreat --- surface DEM --- elevation change --- Sentinel --- Secchi disk --- chlorophyll a --- sediments --- phytoplankton --- floods --- remote sensing --- GIS --- disaster mapping --- Lower Chenab Plain --- laserscanning --- UAV—structure from Motion --- multi-spectral satellite data --- synthetic Aperture Radar --- glacier lake evolution --- glacier river --- slope processes --- rock fall --- cryosphere --- fusion --- inundation probability --- Hurricane Harvey --- ADCIRC --- n/a --- UAV-structure from Motion

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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.

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