Super-Resolution for Remote Sensing Applications Using Deep Learning Techniques
Super-Resolution for Remote Sensing Applications Using Deep Learning Techniques

Author: G. Rohith

Publisher: Cambridge Scholars Publishing

Published: 2022-12-14

Total Pages: 226

ISBN-13: 1527591352

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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.

Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images
Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images

Author: Yakoub Bazi

Publisher: MDPI

Published: 2021-06-15

Total Pages: 438

ISBN-13: 3036509860

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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.

Fundamentals and Methods of Machine and Deep Learning
Fundamentals and Methods of Machine and Deep Learning

Author: Pradeep Singh

Publisher: John Wiley & Sons

Published: 2022-02-01

Total Pages: 480

ISBN-13: 1119821886

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FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.

Hyperspectral Image Analysis
Hyperspectral Image Analysis

Author: Saurabh Prasad

Publisher: Springer Nature

Published: 2020-04-27

Total Pages: 464

ISBN-13: 3030386171

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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.

Computer Vision – ECCV 2018
Computer Vision – ECCV 2018

Author: Vittorio Ferrari

Publisher: Springer

Published: 2018-10-07

Total Pages: 831

ISBN-13: 9783030012397

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The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

Super-Resolution for Remote Sensing Applications Using Deep Learning Techniques
Super-Resolution for Remote Sensing Applications Using Deep Learning Techniques

Author: G. Rohith

Publisher:

Published: 2023-02

Total Pages: 0

ISBN-13: 9781527591349

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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.

Deep Learning for the Earth Sciences
Deep Learning for the Earth Sciences

Author: Gustau Camps-Valls

Publisher: John Wiley & Sons

Published: 2021-08-18

Total Pages: 436

ISBN-13: 1119646162

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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.

Neural Networks: Tricks of the Trade
Neural Networks: Tricks of the Trade

Author: Grégoire Montavon

Publisher: Springer

Published: 2012-11-14

Total Pages: 753

ISBN-13: 3642352898

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The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.

Very High Resolution (VHR) Satellite Imagery
Very High Resolution (VHR) Satellite Imagery

Author: Francisco Eugenio

Publisher: MDPI

Published: 2019-11-06

Total Pages: 262

ISBN-13: 3039217569

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Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing.