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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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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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Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others.

Research & information: general --- Geography --- geographic information system (GIS) --- pocket beaches --- coastal management --- Interreg --- climate change --- remote sensing --- drone --- Sicily --- Malta --- Gozo --- Comino --- systematic literature review --- anomaly intrusion detection --- deep learning --- IoT --- resource constraint --- IDS --- evapotranspiration --- penman-monteith equation --- artificial neural network --- canopy conductance --- Ziz basin --- water quality --- satellite image analysis --- modeling approach --- nitrate --- dissolved oxygen --- chlorophyll a --- time series analysis --- environmental monitoring --- water extraction --- modified normalized difference water index (MNDWI) --- machine learning algorithm --- hyperspectral --- proximal sensing --- panicle initiation --- normalized difference vegetation index (NDVI) --- green ring --- internode-elongation --- Sentinel 1 and 2 --- Copernicus Sentinels --- crop classification --- food security --- agricultural monitoring --- data analysis --- SAR --- random forest --- 3D bale wrapping method --- equal bale dimensions --- mathematical model --- minimal film consumption --- optimal bale dimensions --- round bales --- Sentinel-2 --- SVM --- RF --- Boufakrane River watershed --- irrigation requirements --- water resources --- sustainable land use --- agriculture --- invasive plants --- precision agriculture --- rice farming --- site-specific weed management --- nitrogen prediction --- 1D convolution neural networks --- cucumber --- crop yield improvement --- mango leaf --- CCA --- vein pattern --- leaf disease --- cubic SVM --- chlorophyll-a concentration --- transfer learning --- overfitting --- data augmentation --- guava disease --- plant disease detection --- crops diseases --- entropy --- features fusion --- machine learning --- object-based classification --- density estimation --- histogram --- land use --- crop fields --- soil tillage --- data fusion --- multispectral --- sensor --- probe --- temperature profile --- forest roads --- simulation --- autonomous robots --- smart agriculture --- environmental protection --- photogrammetry --- path planning --- internet of things --- modeling --- convolutional neural networks --- machine vision --- computer vision --- modular robot --- selective spraying --- vision-based crop and weed detection --- Faster R-CNN --- YOLOv5 --- band selection --- CNN --- NDVI --- hyperspectral imaging --- crops --- urban flood --- Sentinel-1a --- Synthetic Aperture Radar (SAR) --- 3D Convolutional Neural Network --- multi-temporal data --- land use classification --- GIS --- Coatzacoalcos --- algorithms --- clustering --- pest control --- site-specific --- virtual pests --- rice plant --- weed --- hyperspectral imagery --- sustainable agriculture --- green technologies --- Internet of Things --- natural resources --- sustainable environment --- IoT ecosystem --- hyperspectral remoting sensing --- crop mapping --- image classification --- deep transfer learning --- hyperparameter optimization --- metaheuristic --- soil attribute --- ordinary Kriging --- rational sampling numbers --- spatial heterogeneity --- sampling --- soil pH --- spatial variation --- ordinary kriging --- Land Use/Land Cover --- LISS-III --- Landsat --- Vision Transformer --- Bidirectional long-short term memory --- Google Earth Engine --- Explainable Artificial Intelligence