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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 --- 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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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

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Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.

convolutional neural network --- image segmentation --- multi-scale feature fusion --- semantic features --- Gaofen 6 --- aerial images --- land-use --- Tai’an --- convolutional neural networks (CNNs) --- feature fusion --- ship detection --- optical remote sensing images --- end-to-end detection --- transfer learning --- remote sensing --- single shot multi-box detector (SSD) --- You Look Only Once-v3 (YOLO-v3) --- Faster RCNN --- statistical features --- Gaofen-2 imagery --- winter wheat --- post-processing --- spatial distribution --- Feicheng --- China --- light detection and ranging --- LiDAR --- deep learning --- convolutional neural networks --- CNNs --- mask regional-convolutional neural networks --- mask R-CNN --- digital terrain analysis --- resource extraction --- hyperspectral image classification --- few-shot learning --- quadruplet loss --- dense network --- dilated convolutional network --- artificial neural networks --- classification --- superstructure optimization --- mixed-inter nonlinear programming --- hyperspectral images --- super-resolution --- SRGAN --- model generalization --- image downscaling --- mixed forest --- multi-label segmentation --- semantic segmentation --- unmanned aerial vehicles --- classification ensemble --- machine learning --- Sentinel-2 --- geographic information system (GIS) --- earth observation --- on-board --- microsat --- mission --- nanosat --- AI on the edge --- CNN

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Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.

Research & information: general --- convolutional neural network --- image segmentation --- multi-scale feature fusion --- semantic features --- Gaofen 6 --- aerial images --- land-use --- Tai’an --- convolutional neural networks (CNNs) --- feature fusion --- ship detection --- optical remote sensing images --- end-to-end detection --- transfer learning --- remote sensing --- single shot multi-box detector (SSD) --- You Look Only Once-v3 (YOLO-v3) --- Faster RCNN --- statistical features --- Gaofen-2 imagery --- winter wheat --- post-processing --- spatial distribution --- Feicheng --- China --- light detection and ranging --- LiDAR --- deep learning --- convolutional neural networks --- CNNs --- mask regional-convolutional neural networks --- mask R-CNN --- digital terrain analysis --- resource extraction --- hyperspectral image classification --- few-shot learning --- quadruplet loss --- dense network --- dilated convolutional network --- artificial neural networks --- classification --- superstructure optimization --- mixed-inter nonlinear programming --- hyperspectral images --- super-resolution --- SRGAN --- model generalization --- image downscaling --- mixed forest --- multi-label segmentation --- semantic segmentation --- unmanned aerial vehicles --- classification ensemble --- machine learning --- Sentinel-2 --- geographic information system (GIS) --- earth observation --- on-board --- microsat --- mission --- nanosat --- AI on the edge --- CNN --- convolutional neural network --- image segmentation --- multi-scale feature fusion --- semantic features --- Gaofen 6 --- aerial images --- land-use --- Tai’an --- convolutional neural networks (CNNs) --- feature fusion --- ship detection --- optical remote sensing images --- end-to-end detection --- transfer learning --- remote sensing --- single shot multi-box detector (SSD) --- You Look Only Once-v3 (YOLO-v3) --- Faster RCNN --- statistical features --- Gaofen-2 imagery --- winter wheat --- post-processing --- spatial distribution --- Feicheng --- China --- light detection and ranging --- LiDAR --- deep learning --- convolutional neural networks --- CNNs --- mask regional-convolutional neural networks --- mask R-CNN --- digital terrain analysis --- resource extraction --- hyperspectral image classification --- few-shot learning --- quadruplet loss --- dense network --- dilated convolutional network --- artificial neural networks --- classification --- superstructure optimization --- mixed-inter nonlinear programming --- hyperspectral images --- super-resolution --- SRGAN --- model generalization --- image downscaling --- mixed forest --- multi-label segmentation --- semantic segmentation --- unmanned aerial vehicles --- classification ensemble --- machine learning --- Sentinel-2 --- geographic information system (GIS) --- earth observation --- on-board --- microsat --- mission --- nanosat --- AI on the edge --- CNN

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This Special Issue includes 20 contributions from across the world with very interesting and current research topics, such as insulation solutions and CO2 emissions; thermal transmittance of LSF walls; statistics for China’s building energy consumption; natural ventilation; thermal behavior of an earthbag building; thermal performance and comfort in a vernacular building; overheating risk under future extreme weather conditions; analytical methods to estimate the thermal transmittance of LSF walls; model simplification on energy and comfort simulation analysis; Trombe wall thermal behavior and energy efficiency of an LSF compartment; new metering hot box for in situ hygrothermal measurement; mechanical and thermal performance of compressed earth blocks; life-cycle assessment of a new house; energy analyses of Serbian buildings with horizontal overhangs; thermal properties of mortar blocks by using recycled glass; prediction of cooling energy consumption building using machine learning techniques; occupants’ behavior, climate change, heating, and cooling energy needs of buildings; a new method for establishing a hygrothermally controlled test room; nonintrusive measurements to incorporate the air renovations in dynamic models; and retrofit of existing buildings with aerogel panels.

Research & information: general --- Technology: general issues --- energy demand analysis --- insulation materials --- climate zones --- envelope --- CO2 emissions --- LSF construction --- facade wall --- partition wall --- thermal transmittance --- thermal bridges --- parametric study --- numerical simulations --- building energy statistics --- building energy consumption --- energy balance sheet --- building energy efficiency --- China --- building automation systems --- heating control --- energy savings --- earth building --- thermal comfort --- passive design --- monitoring and simulation --- glazed balcony --- indoor comfort --- passive strategies --- thermal performance --- vernacular architecture --- overheating risk --- evaluation --- cold climates --- lightweight steel frame --- LSF walls --- U-value --- analytical methods --- calculation procedures --- accuracy --- Model simplifications --- Thermal and visual comfort --- Energy performance --- IDA ICE --- Residential building --- passive solar --- Trombe wall --- light steel frame --- thermal behavior --- energy efficiency --- Mediterranean climate --- office use --- residential use --- heating set-points --- metering hot box --- in situ --- hygrothermal measurement --- dynamic conditions --- historic masonries --- HeLLo --- compressed earth blocks (CEBs) --- compressive strength --- durability --- guarded hot box --- LCA --- environmental impact --- house --- building envelope --- building --- overhangs --- energy consumption --- optimization --- GenOpt --- EnergyPlus --- crushed glass --- periodic thermal transmittance --- energy demand --- adaptive comfort --- social housing --- heating and cooling system --- optimization and management --- energy use prediction --- neural network --- support vector machine --- occupant behavior --- climate changes --- energy needs --- ventilation --- residential buildings --- DesignBuilder --- water vapor resistivity --- hygrothermal modeling --- condensation --- mold --- hygrothermal properties --- moisture transport --- inter-laboratory testing --- building energy --- performance assessment --- air renovation --- non-intrusive measurements --- on-board monitoring --- aerogel --- dynamic simulation --- retrofitting --- economic analysis --- energy demand analysis --- insulation materials --- climate zones --- envelope --- CO2 emissions --- LSF construction --- facade wall --- partition wall --- thermal transmittance --- thermal bridges --- parametric study --- numerical simulations --- building energy statistics --- building energy consumption --- energy balance sheet --- building energy efficiency --- China --- building automation systems --- heating control --- energy savings --- earth building --- thermal comfort --- passive design --- monitoring and simulation --- glazed balcony --- indoor comfort --- passive strategies --- thermal performance --- vernacular architecture --- overheating risk --- evaluation --- cold climates --- lightweight steel frame --- LSF walls --- U-value --- analytical methods --- calculation procedures --- accuracy --- Model simplifications --- Thermal and visual comfort --- Energy performance --- IDA ICE --- Residential building --- passive solar --- Trombe wall --- light steel frame --- thermal behavior --- energy efficiency --- Mediterranean climate --- office use --- residential use --- heating set-points --- metering hot box --- in situ --- hygrothermal measurement --- dynamic conditions --- historic masonries --- HeLLo --- compressed earth blocks (CEBs) --- compressive strength --- durability --- guarded hot box --- LCA --- environmental impact --- house --- building envelope --- building --- overhangs --- energy consumption --- optimization --- GenOpt --- EnergyPlus --- crushed glass --- periodic thermal transmittance --- energy demand --- adaptive comfort --- social housing --- heating and cooling system --- optimization and management --- energy use prediction --- neural network --- support vector machine --- occupant behavior --- climate changes --- energy needs --- ventilation --- residential buildings --- DesignBuilder --- water vapor resistivity --- hygrothermal modeling --- condensation --- mold --- hygrothermal properties --- moisture transport --- inter-laboratory testing --- building energy --- performance assessment --- air renovation --- non-intrusive measurements --- on-board monitoring --- aerogel --- dynamic simulation --- retrofitting --- economic analysis