Super-Resolution for Remote Sensing
Author: Michal Kawulok
Publisher: Springer Nature
Published:
Total Pages: 392
ISBN-13: 3031681061
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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
DOWNLOAD EBOOKSatellite 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.
Computer Vision – ECCV 2018 Workshops
Author: Laura Leal-Taixé
Publisher: Springer
Published: 2019-01-22
Total Pages: 777
ISBN-13: 3030110214
DOWNLOAD EBOOKThe six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in conjunction with the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. The workshop topics present a good orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and novel applications.
Hyperspectral Remote Sensing
Author: Prem Chandra Pandey
Publisher: Elsevier
Published: 2020-08-05
Total Pages: 508
ISBN-13: 0081028954
DOWNLOAD EBOOKHyperspectral Remote Sensing: Theory and Applications offers the latest information on the techniques, advances and wide-ranging applications of hyperspectral remote sensing, such as forestry, agriculture, water resources, soil and geology, among others. The book also presents hyperspectral data integration with other sources, such as LiDAR, Multi-spectral data, and other remote sensing techniques. Researchers who use this resource will be able to understand and implement the technology and data in their respective fields. As such, it is a valuable reference for researchers and data analysts in remote sensing and Earth Observation fields and those in ecology, agriculture, hydrology and geology. - Includes the theory of hyperspectral remote sensing, along with techniques and applications across a variety of disciplines - Presents the processing, methods and techniques utilized for hyperspectral remote sensing and in-situ data collection - Provides an overview of the state-of-the-art, including algorithms, techniques and case studies
Very High Resolution (VHR) Satellite Imagery
Author: Francisco Eugenio
Publisher: MDPI
Published: 2019-11-06
Total Pages: 262
ISBN-13: 3039217569
DOWNLOAD EBOOKRecently, 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.
Computer Vision – ECCV 2018
Author: Vittorio Ferrari
Publisher: Springer
Published: 2018-10-07
Total Pages: 831
ISBN-13: 9783030012397
DOWNLOAD EBOOKThe 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.
Recognition and Perception of Images
Author: Iftikhar B. Abbasov
Publisher: John Wiley & Sons
Published: 2021-03-23
Total Pages: 466
ISBN-13: 1119750555
DOWNLOAD EBOOKThis book is dedicated to the unique interdisciplinary research of imagery processing, recognition and perception. The contents of this book are based on the concepts of mathematical processing, compositional analysis applied in the art and design, and psychological factors of the information perception process. The conduction of compositional analysis carried out in the course of images processing and recognition, creation of the image project solution and modeling of the conceptual space structures are considered together with the mechanism of their perception. Edited and written by a group of international experts, the practical applications for industry are covered, including the influence of internet memes on social networks and face recognition technology subject to interferences. The algorithms of perception and improving of accuracy necessary for satellite imagery recognition and complex reflection from the object are represented with the use of artificial neural networks. Not just a study in how humans recognize and perceive images, this outstanding new volume delves into how these processes are used in technology for continuously evolving industrial applications. Whether for the veteran scientist or engineer, or for the student, this is a must-have for any library.
Deep Learning for the Earth Sciences
Author: Gustau Camps-Valls
Publisher: John Wiley & Sons
Published: 2021-08-18
Total Pages: 436
ISBN-13: 1119646162
DOWNLOAD EBOOKDEEP 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 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
DOWNLOAD EBOOKThe 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.
Super-Resolution Imaging
Author: Peyman Milanfar
Publisher: CRC Press
Published: 2017-12-19
Total Pages: 521
ISBN-13: 135183388X
DOWNLOAD EBOOKWith the exponential increase in computing power and broad proliferation of digital cameras, super-resolution imaging is poised to become the next "killer app." The growing interest in this technology has manifested itself in an explosion of literature on the subject. Super-Resolution Imaging consolidates key recent research contributions from eminent scholars and practitioners in this area and serves as a starting point for exploration into the state of the art in the field. It describes the latest in both theoretical and practical aspects of direct relevance to academia and industry, providing a base of understanding for future progress. Features downloadable tools to supplement material found in the book Recent advances in camera sensor technology have led to an increasingly larger number of pixels being crammed into ever-smaller spaces. This has resulted in an overall decline in the visual quality of recorded content, necessitating improvement of images through the use of post-processing. Providing a snapshot of the cutting edge in super-resolution imaging, this book focuses on methods and techniques to improve images and video beyond the capabilities of the sensors that acquired them. It covers: History and future directions of super-resolution imaging Locally adaptive processing methods versus globally optimal methods Modern techniques for motion estimation How to integrate robustness Bayesian statistical approaches Learning-based methods Applications in remote sensing and medicine Practical implementations and commercial products based on super-resolution The book concludes by concentrating on multidisciplinary applications of super-resolution for a variety of fields. It covers a wide range of super-resolution imaging implementation techniques, including variational, feature-based, multi-channel, learning-based, locally adaptive, and nonparametric methods. This versatile book can be used as the basis for short courses for engineers and scientists, or as part of graduate-level courses in image processing.
