Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images

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 le...

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Bibliographic Details
Main Author: Bazi, Yakoub
Other Authors: Pasolli, Edoardo
Format: eBook
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
Xbd
Cnn
Sar
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
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300 |a 1 electronic resource (438 p.) 
653 |a remote sensing imagery 
653 |a machine learning 
653 |a DenseUNet 
653 |a min-max entropy 
653 |a 3D information 
653 |a feature fusion 
653 |a building damage assessment 
653 |a data augmentation 
653 |a deep learning 
653 |a super-resolution 
653 |a desert 
653 |a convolution 
653 |a Landsat-8 
653 |a neural networks 
653 |a wildfire detection 
653 |a depthwise atrous convolution 
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653 |a two stream residual network 
653 |a Sinkhorn loss 
653 |a multi-scale 
653 |a misalignments 
653 |a faster region-based convolutional neural network (FRCNN) 
653 |a Unmanned Aerial Vehicles (UAV) 
653 |a Research & information: general / bicssc 
653 |a pixel-wise classification 
653 |a deep convolutional networks 
653 |a feature engineering 
653 |a post-disaster 
653 |a convolutional neural networks 
653 |a global convolution network 
653 |a unsupervised segmentation 
653 |a CycleGAN 
653 |a xBD 
653 |a ISPRS vaihingen 
653 |a text image matching 
653 |a image classification 
653 |a visibility 
653 |a infrastructure 
653 |a anomaly detection 
653 |a despeckling 
653 |a Sentinel–1 
653 |a road 
653 |a conditional random field (CRF) 
653 |a Batch Normalization 
653 |a densenet 
653 |a framework 
653 |a high-resolution representations 
653 |a UAV multispectral images 
653 |a orthophoto 
653 |a precision agriculture 
653 |a lifting scheme 
653 |a plant disease detection 
653 |a result correction 
653 |a mapping 
653 |a edge enhancement 
653 |a monitoring 
653 |a adversarial learning 
653 |a scene classification 
653 |a water identification 
653 |a Open Street Map 
653 |a object-based 
653 |a road extraction 
653 |a deep features 
653 |a generative adversarial networks 
653 |a hyperspectral image classification 
653 |a urban forests 
653 |a LSTM 
653 |a synthetic aperture radar 
653 |a EfficientNets 
653 |a outline extraction 
653 |a satellites 
653 |a object detection 
653 |a nearest feature selector 
653 |a LSTM network 
653 |a single-shot multibox detector (SSD) 
653 |a orthophotos registration 
653 |a satellite 
653 |a high spatial resolution remote sensing 
653 |a open-set domain adaptation 
653 |a water index 
653 |a pareto ranking 
653 |a remote sensing 
653 |a CNN 
653 |a pavement markings 
653 |a single-shot 
653 |a OUDN algorithm 
653 |a high-resolution remote sensing imagery 
653 |a semantic segmentation 
653 |a sub-pixel 
653 |a Generative Adversarial Networks 
653 |a high-resolution remote sensing image 
653 |a U–Net 
653 |a orthophotos segmentation 
653 |a SAR 
653 |a convolutional neural network 
653 |a triplet networks 
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700 1 |a Bazi, Yakoub 
700 1 |a Pasolli, Edoardo 
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520 |a 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.