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Recent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice. When processing images, videos, or other types of multimedia, one needs efficient solutions to perform fast and reliable processing. Computational intelligence is used for medical screening where the detection of disease symptoms is carried out, in prevention monitoring to detect suspicious behavior, in agriculture systems to help with growing plants and animal breeding, in transportation systems for the control of incoming and outgoing transportation, for unmanned vehicles to detect obstacles and avoid collisions, in optics and materials for the detection of surface damage, etc. In many cases, we use developed techniques which help us to recognize some special features. In the context of this innovative research on computational intelligence, the Special Issue “Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments” present an excellent opportunity for the dissemination of recent results and achievements for further innovations and development. It is my pleasure to present this collection of excellent contributions to the research community. - Prof. Marcin Woźniak, Silesian University of Technology, Poland –

Information technology industries --- Traffic sign detection and tracking (TSDR) --- advanced driver assistance system (ADAS) --- computer vision --- 3D convolutional neural networks --- machine learning --- CT brain --- brain hemorrhage --- visual inspection --- one-class classifier --- grow-when-required neural network --- evolving connectionist systems --- automatic design --- bio-inspired techniques --- artificial bee colony --- image analysis --- feature extraction --- ship classification --- marine systems --- citrus --- pests and diseases identification --- convolutional neural network --- parameter efficiency --- vehicle detection --- YOLOv2 --- focal loss --- anchor box --- multi-scale --- deep learning --- neural network --- generative adversarial network --- synthetic images --- tool wear monitoring --- superalloy tool --- image recognition --- object detection --- UAV imagery --- vehicular traffic flow detection --- vehicular traffic flow classification --- vehicular traffic congestion --- video classification --- benchmark --- semantic segmentation --- atrous convolution --- spatial pooling --- ship radiated noise --- underwater acoustics --- surface electromyography (sEMG) --- convolution neural networks (CNNs) --- hand gesture recognition --- fabric defect --- mixed kernels --- cross-scale --- cascaded center-ness --- deformable localization --- continuous casting --- surface defects --- 3D imaging --- defect detection --- object detector --- object tracking --- activity measure --- Yolo --- deep sort --- Hungarian algorithm --- optical flows --- spatiotemporal interest points --- sports scene --- CT images --- convolutional neural networks --- hepatic cancer --- visual question answering --- three-dimensional (3D) vision --- reinforcement learning --- human–robot interaction --- few shot learning --- SVM --- CNN --- cascade classifier --- video surveillance --- RFI --- artefacts --- InSAR --- image processing --- pixel convolution --- thresholding --- nearest neighbor filtering --- data acquisition --- augmented reality --- pose estimation --- industrial environments --- information retriever sensor --- multi-hop reasoning --- evidence chains --- complex search request --- high-speed trains --- hunting --- non-stationary --- feature fusion --- multi-sensor fusion --- unmanned aerial vehicles --- drone detection --- UAV detection --- visual detection --- n/a --- human-robot interaction

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• Reference Manager
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Recent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice. When processing images, videos, or other types of multimedia, one needs efficient solutions to perform fast and reliable processing. Computational intelligence is used for medical screening where the detection of disease symptoms is carried out, in prevention monitoring to detect suspicious behavior, in agriculture systems to help with growing plants and animal breeding, in transportation systems for the control of incoming and outgoing transportation, for unmanned vehicles to detect obstacles and avoid collisions, in optics and materials for the detection of surface damage, etc. In many cases, we use developed techniques which help us to recognize some special features. In the context of this innovative research on computational intelligence, the Special Issue “Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments” present an excellent opportunity for the dissemination of recent results and achievements for further innovations and development. It is my pleasure to present this collection of excellent contributions to the research community. - Prof. Marcin Woźniak, Silesian University of Technology, Poland –

Traffic sign detection and tracking (TSDR) --- advanced driver assistance system (ADAS) --- computer vision --- 3D convolutional neural networks --- machine learning --- CT brain --- brain hemorrhage --- visual inspection --- one-class classifier --- grow-when-required neural network --- evolving connectionist systems --- automatic design --- bio-inspired techniques --- artificial bee colony --- image analysis --- feature extraction --- ship classification --- marine systems --- citrus --- pests and diseases identification --- convolutional neural network --- parameter efficiency --- vehicle detection --- YOLOv2 --- focal loss --- anchor box --- multi-scale --- deep learning --- neural network --- generative adversarial network --- synthetic images --- tool wear monitoring --- superalloy tool --- image recognition --- object detection --- UAV imagery --- vehicular traffic flow detection --- vehicular traffic flow classification --- vehicular traffic congestion --- video classification --- benchmark --- semantic segmentation --- atrous convolution --- spatial pooling --- ship radiated noise --- underwater acoustics --- surface electromyography (sEMG) --- convolution neural networks (CNNs) --- hand gesture recognition --- fabric defect --- mixed kernels --- cross-scale --- cascaded center-ness --- deformable localization --- continuous casting --- surface defects --- 3D imaging --- defect detection --- object detector --- object tracking --- activity measure --- Yolo --- deep sort --- Hungarian algorithm --- optical flows --- spatiotemporal interest points --- sports scene --- CT images --- convolutional neural networks --- hepatic cancer --- visual question answering --- three-dimensional (3D) vision --- reinforcement learning --- human–robot interaction --- few shot learning --- SVM --- CNN --- cascade classifier --- video surveillance --- RFI --- artefacts --- InSAR --- image processing --- pixel convolution --- thresholding --- nearest neighbor filtering --- data acquisition --- augmented reality --- pose estimation --- industrial environments --- information retriever sensor --- multi-hop reasoning --- evidence chains --- complex search request --- high-speed trains --- hunting --- non-stationary --- feature fusion --- multi-sensor fusion --- unmanned aerial vehicles --- drone detection --- UAV detection --- visual detection --- n/a --- human-robot interaction

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The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer—at least partially—such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.

Research & information: general --- synthetic aperture radar --- despeckling --- multi-scale --- LSTM --- sub-pixel --- high-resolution remote sensing imagery --- road extraction --- machine learning --- DenseUNet --- scene classification --- lifting scheme --- convolution --- CNN --- image classification --- deep features --- hand-crafted features --- Sinkhorn loss --- remote sensing --- text image matching --- triplet networks --- EfficientNets --- LSTM network --- convolutional neural network --- water identification --- water index --- semantic segmentation --- high-resolution remote sensing image --- pixel-wise classification --- result correction --- conditional random field (CRF) --- satellite --- object detection --- neural networks --- single-shot --- deep learning --- global convolution network --- feature fusion --- depthwise atrous convolution --- high-resolution representations --- ISPRS vaihingen --- Landsat-8 --- faster region-based convolutional neural network (FRCNN) --- single-shot multibox detector (SSD) --- super-resolution --- remote sensing imagery --- edge enhancement --- satellites --- open-set domain adaptation --- adversarial learning --- min-max entropy --- pareto ranking --- SAR --- Sentinel–1 --- Open Street Map --- U–Net --- desert --- road --- infrastructure --- mapping --- monitoring --- deep convolutional networks --- outline extraction --- misalignments --- nearest feature selector --- hyperspectral image classification --- two stream residual network --- Batch Normalization --- plant disease detection --- precision agriculture --- UAV multispectral images --- orthophotos registration --- 3D information --- orthophotos segmentation --- wildfire detection --- convolutional neural networks --- densenet --- generative adversarial networks --- CycleGAN --- data augmentation --- pavement markings --- visibility --- framework --- urban forests --- OUDN algorithm --- object-based --- high spatial resolution remote sensing --- Generative Adversarial Networks --- post-disaster --- building damage assessment --- anomaly detection --- Unmanned Aerial Vehicles (UAV) --- xBD --- feature engineering --- orthophoto --- unsupervised segmentation