Advanced Deep Learning Strategies For The Analysis Of Remote Sensing Images

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Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images

Yakoub Bazi,Edoardo Pasolli
Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images Book PDF

Author : Yakoub Bazi,Edoardo Pasolli
Publisher : MDPI
Page : 438 pages
File Size : 54,8 Mb
Release : 2021-06-15
Category : Science
ISBN : 9783036509860

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Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images by Yakoub Bazi,Edoardo Pasolli Pdf

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.

Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images

Yakoub Bazi,Edoardo Pasolli
Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images Book PDF

Author : Yakoub Bazi,Edoardo Pasolli
Publisher : Unknown
Page : 438 pages
File Size : 44,6 Mb
Release : 2021
Category : Electronic
ISBN : 3036509879

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Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images by Yakoub Bazi,Edoardo Pasolli Pdf

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.

Advances in Machine Learning and Image Analysis for GeoAI

Saurabh Prasad,Jocelyn Chanussot,Jun Li
Advances in Machine Learning and Image Analysis for GeoAI Book PDF

Author : Saurabh Prasad,Jocelyn Chanussot,Jun Li
Publisher : Elsevier
Page : 366 pages
File Size : 47,7 Mb
Release : 2024-06-01
Category : Science
ISBN : 9780443190780

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Advances in Machine Learning and Image Analysis for GeoAI by Saurabh Prasad,Jocelyn Chanussot,Jun Li Pdf

Advances in Machine Learning and Image Analysis for GeoAI provides state-of-the-art machine learning and signal processing techniques for a comprehensive collection of geospatial sensors and sensing platforms. The book covers supervised, semi-supervised and unsupervised geospatial image analysis, sensor fusion across modalities, image super-resolution, transfer learning across sensors and time-points, and spectral unmixing among other topics. The chapters in these thematic areas cover a variety of algorithmic frameworks such as variants of convolutional neural networks, graph convolutional networks, multi-stream networks, Bayesian networks, generative adversarial networks, transformers and more.Advances in Machine Learning and Image Analysis for GeoAI provides graduate students, researchers and practitioners in the area of signal processing and geospatial image analysis with the latest techniques to implement deep learning strategies in their research. Covers the latest machine learning and signal processing techniques that can effectively leverage geospatial imagery at scale Presents a variety of algorithmic frameworks, including variants of convolutional neural networks, multi-stream networks, Bayesian networks, and more Includes open-source code-base for algorithms described in each chapter

Learning to Understand Remote Sensing Images

Qi Wang
Learning to Understand Remote Sensing Images Book PDF

Author : Qi Wang
Publisher : MDPI
Page : 426 pages
File Size : 48,5 Mb
Release : 2019-09-30
Category : Computers
ISBN : 9783038976844

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Learning to Understand Remote Sensing Images by Qi Wang Pdf

With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Super-Resolution for Remote Sensing Applications Using Deep Learning Techniques

G. Rohith,G. Lakshmi Sutha
Super Resolution for Remote Sensing Applications Using Deep Learning Techniques Book PDF

Author : G. Rohith,G. Lakshmi Sutha
Publisher : Cambridge Scholars Publishing
Page : 226 pages
File Size : 55,9 Mb
Release : 2022-12-14
Category : Computers
ISBN : 9781527591356

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Super-Resolution for Remote Sensing Applications Using Deep Learning Techniques by G. Rohith,G. Lakshmi Sutha Pdf

Satellite image processing is crucial in detecting vegetation, clouds, and other atmospheric applications. Due to sensor limitations and pre-processing, remotely sensed satellite images may have interpretability concerns as to specific portions of the image, making it hard to recognise patterns or objects and posing the risk of losing minute details in the image. Existing imaging processors and optical components are expensive to counterfeit, have interpretability issues, and are not necessarily viable in real applications. This book exploits the usage of deep learning (DL) components in feature extraction to boost the minute details of images and their classification implications to tackle such problems. It shows the importance of super-resolution in improving the spatial details of images and aiding digital aerial photography in pan-sharpening, detecting signatures correctly, and making precise decisions with decision-making tools.

Hyperspectral Image Analysis

Saurabh Prasad,Jocelyn Chanussot
Hyperspectral Image Analysis Book PDF

Author : Saurabh Prasad,Jocelyn Chanussot
Publisher : Springer Nature
Page : 464 pages
File Size : 52,6 Mb
Release : 2020-04-27
Category : Computers
ISBN : 9783030386177

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Hyperspectral Image Analysis by Saurabh Prasad,Jocelyn Chanussot Pdf

This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.

Learning to Understand Remote Sensing Images

Qi Wang
Learning to Understand Remote Sensing Images Book PDF

Author : Qi Wang
Publisher : MDPI
Page : 376 pages
File Size : 53,7 Mb
Release : 2019-09-30
Category : Computers
ISBN : 9783038976981

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Learning to Understand Remote Sensing Images by Qi Wang Pdf

With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Deep Learning for Hyperspectral Image Analysis and Classification

Linmi Tao,Atif Mughees
Deep Learning for Hyperspectral Image Analysis and Classification Book PDF

Author : Linmi Tao,Atif Mughees
Publisher : Springer Nature
Page : 207 pages
File Size : 42,6 Mb
Release : 2021-02-20
Category : Computers
ISBN : 9789813344204

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Deep Learning for Hyperspectral Image Analysis and Classification by Linmi Tao,Atif Mughees Pdf

This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

Deep Learning for Remote Sensing Images with Open Source Software

Rémi Cresson
Deep Learning for Remote Sensing Images with Open Source Software Book PDF

Author : Rémi Cresson
Publisher : CRC Press
Page : 165 pages
File Size : 51,7 Mb
Release : 2020-07-15
Category : Technology & Engineering
ISBN : 9781000093599

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Deep Learning for Remote Sensing Images with Open Source Software by Rémi Cresson Pdf

In today’s world, deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However, most people are missing the educational tools to make use of this resource. Deep Learning for Remote Sensing Images with Open Source Software is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing, including landcover mapping, forestry, urban studies, disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data. Specific Features of this Book: The first book that explains how to apply deep learning techniques to public, free available data (Spot-7 and Sentinel-2 images, OpenStreetMap vector data), using open source software (QGIS, Orfeo ToolBox, TensorFlow) Presents approaches suited for real world images and data targeting large scale processing and GIS applications Introduces state of the art deep learning architecture families that can be applied to remote sensing world, mainly for landcover mapping, but also for generic approaches (e.g. image restoration) Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills. Includes deep learning techniques through many step by step remote sensing data processing exercises.

Image Analysis, Classification and Change Detection in Remote Sensing

Morton John Canty
Image Analysis Classification and Change Detection in Remote Sensing Book PDF

Author : Morton John Canty
Publisher : CRC Press
Page : 508 pages
File Size : 55,8 Mb
Release : 2019-03-11
Category : Technology & Engineering
ISBN : 9780429875359

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Image Analysis, Classification and Change Detection in Remote Sensing by Morton John Canty Pdf

Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python, Fourth Edition, is focused on the development and implementation of statistically motivated, data-driven techniques for digital image analysis of remotely sensed imagery and it features a tight interweaving of statistical and machine learning theory of algorithms with computer codes. It develops statistical methods for the analysis of optical/infrared and synthetic aperture radar (SAR) imagery, including wavelet transformations, kernel methods for nonlinear classification, as well as an introduction to deep learning in the context of feed forward neural networks. New in the Fourth Edition: An in-depth treatment of a recent sequential change detection algorithm for polarimetric SAR image time series. The accompanying software consists of Python (open source) versions of all of the main image analysis algorithms. Presents easy, platform-independent software installation methods (Docker containerization). Utilizes freely accessible imagery via the Google Earth Engine and provides many examples of cloud programming (Google Earth Engine API). Examines deep learning examples including TensorFlow and a sound introduction to neural networks, Based on the success and the reputation of the previous editions and compared to other textbooks in the market, Professor Canty’s fourth edition differs in the depth and sophistication of the material treated as well as in its consistent use of computer codes to illustrate the methods and algorithms discussed. It is self-contained and illustrated with many programming examples, all of which can be conveniently run in a web browser. Each chapter concludes with exercises complementing or extending the material in the text.

Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS

Chang-Wook Lee,Hyangsun Han,Hoonyol Lee
Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS Book PDF

Author : Chang-Wook Lee,Hyangsun Han,Hoonyol Lee
Publisher : Mdpi AG
Page : 166 pages
File Size : 49,5 Mb
Release : 2021-11-11
Category : Science
ISBN : 3036516042

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Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS by Chang-Wook Lee,Hyangsun Han,Hoonyol Lee Pdf

This book is based on Special Issue "Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS" from early 2020 to 2021. This book includes seven papers related to the application of artificial intelligence, machine learning and deep learning algorithms using remote sensing and GIS techniques in urban areas.

Deep Learning for the Earth Sciences

Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein
Deep Learning for the Earth Sciences Book PDF

Author : Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein
Publisher : John Wiley & Sons
Page : 436 pages
File Size : 41,9 Mb
Release : 2021-08-18
Category : Technology & Engineering
ISBN : 9781119646167

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Deep Learning for the Earth Sciences by Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein Pdf

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

Advanced Machine Learning and Deep Learning Approaches for Remote Sensing

Gwanggil Jeon
Advanced Machine Learning and Deep Learning Approaches for Remote Sensing Book PDF

Author : Gwanggil Jeon
Publisher : Mdpi AG
Page : 0 pages
File Size : 52,7 Mb
Release : 2023-06-20
Category : Science
ISBN : 303657946X

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Advanced Machine Learning and Deep Learning Approaches for Remote Sensing by Gwanggil Jeon Pdf

This reprint provides research on how technologies such as artificial intelligence-based machine learning and deep learning can be applied to remote sensing. Through this, we can see the process of solving the existing problems of image and image signal processing for remote sensing. These techniques are computationally intensive and require the help of high-performance computing devices. With the development of devices such as GPUs, remote sensing technology, and aerial sensing technology, it is possible to monitor the Earth with high-resolution images and to obtain vast amounts of Earth observation data. The papers published in this reprint describe recent advances in big data processing and artificial intelligence-based technologies for remote sensing technology.

Signal and Image Processing for Remote Sensing

C.H. Chen
Signal and Image Processing for Remote Sensing Book PDF

Author : C.H. Chen
Publisher : CRC Press
Page : 433 pages
File Size : 52,5 Mb
Release : 2024-06-11
Category : Technology & Engineering
ISBN : 9781040031254

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Signal and Image Processing for Remote Sensing by C.H. Chen Pdf

Advances in signal and image processing for remote sensing have been tremendous in recent years. The progress has been particularly significant with the use of deep learning based techniques to solve remote sensing problems. These advancements are the focus of this third edition of Signal and Image Processing for Remote Sensing. It emphasizes the use of machine learning approaches for the extraction of remote sensing information. Other topics include change detection in remote sensing and compressed sensing. With 19 new chapters written by world leaders in the field, this book provides an authoritative examination and offers a unique point of view on signal and image processing. Features Includes all new content and does not replace the previous edition Covers machine learning approaches in both signal and image processing for remote sensing Studies deep learning methods for remote sensing information extraction that is found in other books Explains SAR, microwave, seismic, GPR, and hyperspectral sensors and all sensors considered Discusses improved pattern classification approaches and compressed sensing approaches Provides ample examples of each aspect of both signal and image processing This book is intended for university academics, researchers, postgraduate students, industry, and government professionals who use remote sensing and its applications.

Image Fusion in Remote Sensing

Arian Azarang,Nasser Kehtarnavaz
Image Fusion in Remote Sensing Book PDF

Author : Arian Azarang,Nasser Kehtarnavaz
Publisher : Springer Nature
Page : 89 pages
File Size : 50,9 Mb
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 9783031022562

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Image Fusion in Remote Sensing by Arian Azarang,Nasser Kehtarnavaz Pdf

Image fusion in remote sensing or pansharpening involves fusing spatial (panchromatic) and spectral (multispectral) images that are captured by different sensors on satellites. This book addresses image fusion approaches for remote sensing applications. Both conventional and deep learning approaches are covered. First, the conventional approaches to image fusion in remote sensing are discussed. These approaches include component substitution, multi-resolution, and model-based algorithms. Then, the recently developed deep learning approaches involving single-objective and multi-objective loss functions are discussed. Experimental results are provided comparing conventional and deep learning approaches in terms of both low-resolution and full-resolution objective metrics that are commonly used in remote sensing. The book is concluded by stating anticipated future trends in pansharpening or image fusion in remote sensing.