Artificial Intelligence Techniques For Satellite Image Analysis
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Artificial Intelligence Techniques For Satellite Image Analysis
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Author : D. Jude Hemanth
language : en
Publisher: Springer Nature
Release Date : 2019-11-13
Artificial Intelligence Techniques For Satellite Image Analysis written by D. Jude Hemanth and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-13 with Computers categories.
The main objective of this book is to provide a common platform for diverse concepts in satellite image processing. In particular it presents the state-of-the-art in Artificial Intelligence (AI) methodologies and shares findings that can be translated into real-time applications to benefit humankind. Interdisciplinary in its scope, the book will be of interest to both newcomers and experienced scientists working in the fields of satellite image processing, geo-engineering, remote sensing and Artificial Intelligence. It can be also used as a supplementary textbook for graduate students in various engineering branches related to image processing.
A New Automatic Processing Technique For Satellite Imagery Analysis
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Author : R. S. Hawkins
language : en
Publisher:
Release Date : 1977
A New Automatic Processing Technique For Satellite Imagery Analysis written by R. S. Hawkins and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1977 with Image processing categories.
A new approach to the analysis of satellite imagery is presented. The central part of this approach is an algorithm which compresses information stored in the ordinary six or eight bits per picture element into only one bit. The quality of this compression is demonstrated by examples of its application to high resolution visual imagery. Both visual inspection and rms difference criterion are used for this evaluation. There are four objectives of this report which are: to review the status of processing techniques which remove redundant information, to show the need for redundance reduction in the processing of satellite images, to present the development of an algorithm for reducing it, and to show results obtained by application of the algorithm to visual imagery. Also, comments are made on needed developments of the technique and its potential application to problems of analysis of satellite imagery data. (Author).
An Overview Of Technological Revolution In Satellite Image Analysis
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Author : Jenice Aroma R.
language : en
Publisher: Infinite Study
Release Date :
An Overview Of Technological Revolution In Satellite Image Analysis written by Jenice Aroma R. and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
The satellite image based applications are highly utilized nowadays from simple purposes like vehicle navigation to complex surveillance and virtual environment modeling projects. On increased population rate, the depletion of natural resources is highly unavoidable and it leads to increased threats on natural hazards. In order to protect and overcome the physical losses on devastation of properties, the risk mapping models such as weather forecasts, drought modeling and other hazard assessment models are in need.
Satellite Image Analysis Clustering And Classification
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Author : Surekha Borra
language : en
Publisher: Springer
Release Date : 2019-02-08
Satellite Image Analysis Clustering And Classification written by Surekha Borra and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-08 with Technology & Engineering categories.
Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time. This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering.
Big Data Analytics For Satellite Image Processing And Remote Sensing
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Author : Swarnalatha, P.
language : en
Publisher: IGI Global
Release Date : 2018-03-09
Big Data Analytics For Satellite Image Processing And Remote Sensing written by Swarnalatha, P. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-09 with Technology & Engineering categories.
The scope of image processing and recognition has broadened due to the gap in scientific visualization. Thus, new imaging techniques have developed, and it is imperative to study this progression for optimal utilization. Big Data Analytics for Satellite Image Processing and Remote Sensing is a critical scholarly resource that examines the challenges and difficulties of implementing big data in image processing for remote sensing and related areas. Featuring coverage on a broad range of topics, such as distributed computing, parallel processing, and spatial data, this book is geared towards scientists, professionals, researchers, and academicians seeking current research on the use of big data analytics in satellite image processing and remote sensing.
Artificial Intelligence And Machine Learning In Satellite Data Processing And Services
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Author : Sumit Kumar
language : en
Publisher: Springer Nature
Release Date : 2023-01-02
Artificial Intelligence And Machine Learning In Satellite Data Processing And Services written by Sumit Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-02 with Technology & Engineering categories.
This book, Artificial Intelligence and Machine Learning in Satellite: Data Processing and Services, presents the selected proceedings of the International Conference on Small Satellites (ICSS 2022) that aims to provide an opportunity for academicians, scientists, researchers, and industry experts, engaged in teaching, research, and development on satellite data processing and its services by employing advanced artificial intelligence-based machine learning techniques. This book covers the application of artificial intelligence and machine learning techniques in various domains of earth observations like natural resources and environmental management, water resources, urban and rural development, climate change, and other contemporary subjects. The book will surely be a valuable asset for beginners, researchers, and professionals working in satellite data processing and services using artificial intelligence and machine learning approaches.
Machine And Deep Learning Techniques For Content Extraction Of Satellite Images
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Author : Manami Barthakur
language : en
Publisher:
Release Date : 2023-01-17
Machine And Deep Learning Techniques For Content Extraction Of Satellite Images written by Manami Barthakur and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-17 with categories.
Machine and deep learning techniques for content extraction of satellite images utilize artificial intelligence and neural networks to analyze and extract information from satellite imagery. These techniques can be used for a variety of applications, such as image classification, object detection, optical character recognition (OCR), and semantic segmentation. Convolutional Neural Networks (CNNs) are commonly used for image classification and object detection tasks. These networks are designed to process and understand images by analyzing the spatial relationship between pixels. They are composed of multiple layers, with each layer analyzing a different level of detail in the image. CNNs are particularly effective at identifying patterns and features in satellite images, such as roads, buildings, and vegetation. Recurrent Neural Networks (RNNs) and Long Short-term Memory (LSTM) networks are particularly useful for tasks that require the analysis of sequential data, like time series data. They are particularly useful in land cover change detection, change detection and time series analysis of satellite images. Semantic segmentation is the process of classifying each pixel in an image to a particular class, and it can be achieved using Fully Convolutional Networks (FCN) and U-Net architecture. This technique is particularly useful for identifying different land cover classes in satellite images, such as urban, agricultural, and natural areas. Generative Adversarial Networks (GANs) are used for creating synthetic images or super resolution of images. These are particularly useful for creating synthetic data for training and testing deep learning models for satellite images. Transfer learning is a technique that allows a pre-trained model to be fine-tuned for a specific task. This can be used to improve the accuracy of image classification and object detection tasks by using a pre-trained model as a starting point. In summary, machine and deep learning techniques for content extraction of satellite images involve using neural networks and computer vision techniques to analyze and extract information from satellite imagery. These techniques can be used for a variety of applications, such as image classification, object detection, and semantic segmentation, and can improve the accuracy and efficiency of extracting information from satellite images. to process and understand images by analyzing the spatial relationship between pixels. They are composed of multiple layers, with each layer analyzing a different level of detail in the image. CNNs are particularly effective at identifying patterns and features in satellite images, such as roads, buildings, and vegetation. Recurrent Neural Networks (RNNs) and Long Short-term Memory (LSTM) networks are particularly useful for tasks that require the analysis of sequential data, like time series data. They are particularly useful in land cover change detection, change detection and time series analysis of satellite images. Semantic segmentation is the process of classifying each pixel in an image to a particular class, and it can be achieved using Fully Convolutional Networks (FCN) and U-Net architecture. This technique is particularly useful for identifying different land cover classes in satellite images, such as urban, agricultural, and natural areas. Generative Adversarial Networks (GANs) are used for creating synthetic images or super resolution of images. These are particularly useful for creating synthetic data for training and testing deep learning models for satellite images. Transfer learning is a technique that allows a pre-trained model to be fine-tuned for a specific task. This can be used to improve the accuracy of image classification and object detection.
Advances In Machine Learning And Image Analysis For Geoai
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Author : Saurabh Prasad
language : en
Publisher: Elsevier
Release Date : 2024-06-01
Advances In Machine Learning And Image Analysis For Geoai written by Saurabh Prasad and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-01 with Science categories.
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
Change Detection And Image Time Series Analysis 2
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Author : Abdourrahmane M. Atto
language : en
Publisher: John Wiley & Sons
Release Date : 2021-12-29
Change Detection And Image Time Series Analysis 2 written by Abdourrahmane M. Atto and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-29 with Computers categories.
Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series. Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches. Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns. Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations, Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.
Remote Sensing Digital Image Analysis
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Author : John A. Richards
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Remote Sensing Digital Image Analysis written by John A. Richards and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Technology & Engineering categories.
Revised and enlarged to reflect new developments in the field, the fourth edition of this well-established text provides an introduction to quantitative evaluation of satellite- and aircraft-derived remotely retrieved data. Each chapter covers the pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations.