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Coastal areas are remarkable regions with high spatiotemporal variability. A large population is affected by their physical and biological processes—resulting from effects on tourism to biodiversity and productivity. Coastal ecosystems perform several critical ecosystem services and functions, such as water oxygenation and nutrients provision, seafloor and beach stabilization (as sediment is controlled and trapped within the rhizomes of the seagrass meadows), carbon burial, as areas for nursery, and as refuge for several commercial and endemic species. Knowledge of the spatial distribution of marine habitats is prerequisite information for the conservation and sustainable use of marine resources. Remote sensing from UAVs to spaceborne sensors is offering a unique opportunity to measure, analyze, quantify, map, and explore the processes on the coastal areas at high temporal frequencies. This Special Issue on “Application of Remote Sensing in Coastal Areas” is specifically addresses those successful applications—from local to regional scale—in coastal environments related to ecosystem productivity, biodiversity, sea level rise.

Research & information: general --- Geography --- satellite remote sensing --- Landsat --- coastline --- barrier island --- morphological change --- coastal ocean --- Photon-counting lidar --- MABEL --- land cover --- remote sensing --- signal photons --- ground settlement --- marine reclamation land --- time series InSAR --- Sentinel-1 --- Xiamen New Airport --- Pleiades --- photogrammetry --- LiDAR --- RTK-GPS --- beach topography --- cliff coastlines --- time-series analysis --- terrestrial laser scanner --- southern Baltic Sea --- non-parametric Bayesian network --- satellite-derived bathymetry --- hydrography --- CubeSats --- hypertemporal --- zones of confidence --- PlanetScope --- vegetation mapping --- dunes --- unmanned aerial system --- pixel-based classification --- object-based classification --- dune vegetation classification --- coastal monitoring --- multispectral satellite images --- multi-temporal NDVI --- pixels based supervised classification --- Random Forest --- harmonization --- shoreline mapping --- semi-global subpixel localization --- intensity integral error --- polarimetric SAR --- polarimetric decomposition --- ship detection --- Euclidean distance --- mutual information --- new feature --- Bohai sea ice --- sea ice extent --- OLCI imagery --- sea ice information index --- waterline extraction --- sub-pixel --- surface water mapping --- data cube --- contour extraction --- water extraction --- water indices --- thresholding --- Coastal process --- wind wake --- heat advection --- multi-sensor --- ASAR --- oceanic thermal response --- Hainan Island --- coastal remote sensing --- habitat mapping --- unmanned aerial vehicle (UAV) --- unmanned aircraft system (UAS) --- drone --- object-based image analysis (OBIA) --- UAS data acquisition --- n/a

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A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interesting

Technology: general issues --- on-board data compression --- CCSDS 123.0-B-2 --- near-lossless hyperspectral image compression --- hyperspectral image coding --- graph filterbanks --- integer-to-integer transforms --- graph signal processing --- compact data structure --- quadtree --- k2-tree --- k2-raster --- DACs --- 3D-CALIC --- M-CALIC --- hyperspectral images --- fully convolutional network --- semantic segmentation --- spectral image --- tensor decomposition --- HEVC --- intra coding --- JPEG 2000 --- high bit-depth compression --- multispectral satellite images --- crop classification --- Landsat-8 --- Sentinel-2 --- Elias codes --- Simple9 --- Simple16 --- PForDelta --- Rice codes --- hyperspectral scenes --- hyperspectral image --- lossy compression --- real time --- FPGA --- PCA --- JPEG2000 --- EBCOT --- multispectral --- hyperspectral --- CCSDS --- FAPEC --- data compression --- transform --- hyperspectral imaging --- on-board processing --- GPU --- real-time performance --- UAV --- parallel computing --- remote sensing --- image quality --- image classification --- visual quality metrics --- spectral–spatial feature --- multispectral image compression --- partitioned extraction --- group convolution --- rate-distortion --- compressed sensing --- invertible projection --- coupled dictionary --- singular value --- task-driven learning --- on board compression --- transform coding --- learned compression --- neural networks --- variational autoencoder --- complexity --- real-time compression --- on-board compression --- real-time transmission --- UAVs --- compressive sensing --- synthetic aperture sonar --- underwater sonar imaging --- remote sensing data compression --- lossless compression --- compression impact --- computational complexity

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• Reference Manager
• EndNote
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A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interesting

on-board data compression --- CCSDS 123.0-B-2 --- near-lossless hyperspectral image compression --- hyperspectral image coding --- graph filterbanks --- integer-to-integer transforms --- graph signal processing --- compact data structure --- quadtree --- k2-tree --- k2-raster --- DACs --- 3D-CALIC --- M-CALIC --- hyperspectral images --- fully convolutional network --- semantic segmentation --- spectral image --- tensor decomposition --- HEVC --- intra coding --- JPEG 2000 --- high bit-depth compression --- multispectral satellite images --- crop classification --- Landsat-8 --- Sentinel-2 --- Elias codes --- Simple9 --- Simple16 --- PForDelta --- Rice codes --- hyperspectral scenes --- hyperspectral image --- lossy compression --- real time --- FPGA --- PCA --- JPEG2000 --- EBCOT --- multispectral --- hyperspectral --- CCSDS --- FAPEC --- data compression --- transform --- hyperspectral imaging --- on-board processing --- GPU --- real-time performance --- UAV --- parallel computing --- remote sensing --- image quality --- image classification --- visual quality metrics --- spectral–spatial feature --- multispectral image compression --- partitioned extraction --- group convolution --- rate-distortion --- compressed sensing --- invertible projection --- coupled dictionary --- singular value --- task-driven learning --- on board compression --- transform coding --- learned compression --- neural networks --- variational autoencoder --- complexity --- real-time compression --- on-board compression --- real-time transmission --- UAVs --- compressive sensing --- synthetic aperture sonar --- underwater sonar imaging --- remote sensing data compression --- lossless compression --- compression impact --- computational complexity