Searching For Patterns In Remote Sensing Image Databases Using Neural Networks

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Searching for Patterns in Remote Sensing Image Databases Using Neural Networks

Author : Justin D. Paola
Publisher :
ISBN 13 :
Total Pages : 10 pages
Book Rating : 4.:/5 (471 download)

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Book Synopsis Searching for Patterns in Remote Sensing Image Databases Using Neural Networks by : Justin D. Paola

Download or read book Searching for Patterns in Remote Sensing Image Databases Using Neural Networks written by Justin D. Paola and published by . This book was released on 1995 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "We have investigated a method, based on a successful neural network multispectral image classification system, of searching for single patterns in remote sensing databases. While defining the pattern to search for and the feature to be used for that search (spectral, spatial, temporal, etc.) is challenging, a more difficult task is selecting competing patterns to train against the desired pattern. Schemes for competing pattern selection, including random selection and human interpreted selection, are discussed in the context of an example detection of dense urban areas in Landsat Thematic Mapper imagery. When applying the search to multiple images, a simple normalization method can alleviate the problem of inconsistent image calibration. Another potential problem, that of highly compressed data, was found to have a minimal effect on the ability to detect the desired pattern. The neural network algorithm has been implemented using the PVM (Parallel Virtual Machine) library and nearly-optimal speedups have been obtained that help alleviate the long process of searching through imagery."

Searching for Patterns in Remote Sensing Image Database Using Neural Networks, Justin D. Paola Et Al Research Inst., for Advanced Computer Science, 16 Aug., 1995

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Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (624 download)

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Book Synopsis Searching for Patterns in Remote Sensing Image Database Using Neural Networks, Justin D. Paola Et Al Research Inst., for Advanced Computer Science, 16 Aug., 1995 by :

Download or read book Searching for Patterns in Remote Sensing Image Database Using Neural Networks, Justin D. Paola Et Al Research Inst., for Advanced Computer Science, 16 Aug., 1995 written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification

Author : Anil Kumar
Publisher : CRC Press
ISBN 13 : 1000091546
Total Pages : 177 pages
Book Rating : 4.0/5 ( download)

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Book Synopsis Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification by : Anil Kumar

Download or read book Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification written by Anil Kumar and published by CRC Press. This book was released on 2020-07-19 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.

Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images

Author : Yakoub Bazi
Publisher : MDPI
ISBN 13 : 3036509860
Total Pages : 438 pages
Book Rating : 4.0/5 (365 download)

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Book Synopsis Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images by : Yakoub Bazi

Download or read book Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images written by Yakoub Bazi and published by MDPI. This book was released on 2021-06-15 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Learning for Remote Sensing Images with Open Source Software

Author : Rémi Cresson
Publisher : CRC Press
ISBN 13 : 100009359X
Total Pages : 165 pages
Book Rating : 4.0/5 ( download)

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Book Synopsis Deep Learning for Remote Sensing Images with Open Source Software by : Rémi Cresson

Download or read book Deep Learning for Remote Sensing Images with Open Source Software written by Rémi Cresson and published by CRC Press. This book was released on 2020-07-15 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Databases

Author : Vittorio Castelli
Publisher : John Wiley & Sons
ISBN 13 : 0471464074
Total Pages : 609 pages
Book Rating : 4.4/5 (714 download)

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Book Synopsis Image Databases by : Vittorio Castelli

Download or read book Image Databases written by Vittorio Castelli and published by John Wiley & Sons. This book was released on 2004-04-07 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: The explosive growth of multimedia data transmission has generated a critical need for efficient, high-capacity image databases, as well as powerful search engines to retrieve image data from them. This book brings together contributions by an international all-star team of innovators in the field who share their insights into all key aspects of image database and search engine construction. Readers get in-depth discussions of the entire range of crucial image database architecture, indexing and retrieval, transmission, display, and user interface issues. And, using examples from an array of disciplines, the authors present cutting-edge applications in medical imagery, multimedia communications, earth science, remote sensing, and other major application areas.

Artificial Neural Networks and Evolutionary Computation in Remote Sensing

Author : Taskin Kavzoglu
Publisher : MDPI
ISBN 13 : 3039438271
Total Pages : 256 pages
Book Rating : 4.0/5 (394 download)

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Book Synopsis Artificial Neural Networks and Evolutionary Computation in Remote Sensing by : Taskin Kavzoglu

Download or read book Artificial Neural Networks and Evolutionary Computation in Remote Sensing written by Taskin Kavzoglu and published by MDPI. This book was released on 2021-01-19 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.

Pattern Recognition And Big Data

Author : Sankar Kumar Pal
Publisher : World Scientific
ISBN 13 : 9813144564
Total Pages : 875 pages
Book Rating : 4.8/5 (131 download)

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Book Synopsis Pattern Recognition And Big Data by : Sankar Kumar Pal

Download or read book Pattern Recognition And Big Data written by Sankar Kumar Pal and published by World Scientific. This book was released on 2016-12-15 with total page 875 pages. Available in PDF, EPUB and Kindle. Book excerpt: Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications.Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis.

Pattern Recognition and Artificial Intelligence

Author : Yue Lu
Publisher : Springer Nature
ISBN 13 : 3030598306
Total Pages : 752 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Pattern Recognition and Artificial Intelligence by : Yue Lu

Download or read book Pattern Recognition and Artificial Intelligence written by Yue Lu and published by Springer Nature. This book was released on 2020-10-09 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the Second International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020, which took place in Zhongshan, China, in October 2020. The 49 full and 14 short papers presented were carefully reviewed and selected for inclusion in the book. The papers were organized in topical sections as follows: handwriting and text processing; features and classifiers; deep learning; computer vision and image processing; medical imaging and applications; and forensic studies and medical diagnosis.

Handbook of Pattern Recognition & Computer Vision

Author : Chi-hau Chen
Publisher : World Scientific
ISBN 13 : 9810230710
Total Pages : 1045 pages
Book Rating : 4.8/5 (12 download)

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Book Synopsis Handbook of Pattern Recognition & Computer Vision by : Chi-hau Chen

Download or read book Handbook of Pattern Recognition & Computer Vision written by Chi-hau Chen and published by World Scientific. This book was released on 1999 with total page 1045 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation. Presents the latest research findings in theory, techniques, algorithms, and major applications of pattern recognition and computer vision, as well as new hardware and architecture aspects. Contains sections on basic methods in pattern recognition and computer vision, nine recognition applications, inspection and robotic applications, and architectures and technology. Some areas discussed include cluster analysis, 3D vision of dynamic objects, speech recognition, computer vision in food handling, and video content analysis and retrieval. This second edition is extensively revised to describe progress in the field since 1993. Chen is affiliated with the electrical and computer engineering department at the University of Massachusetts-Dartmouth. Annotation copyrighted by Book News, Inc., Portland, OR.

IGARSS '95

Author :
Publisher : Institute of Electrical & Electronics Engineers(IEEE)
ISBN 13 :
Total Pages : 864 pages
Book Rating : 4.:/5 (318 download)

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Book Synopsis IGARSS '95 by :

Download or read book IGARSS '95 written by and published by Institute of Electrical & Electronics Engineers(IEEE). This book was released on 1995 with total page 864 pages. Available in PDF, EPUB and Kindle. Book excerpt:

RIACS/USRA Annual Report 1995

Author :
Publisher :
ISBN 13 :
Total Pages : 56 pages
Book Rating : 4.:/5 (317 download)

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Book Synopsis RIACS/USRA Annual Report 1995 by :

Download or read book RIACS/USRA Annual Report 1995 written by and published by . This book was released on 1995 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Proceedings

Author :
Publisher : American Society of Civil Engineers
ISBN 13 :
Total Pages : 612 pages
Book Rating : 4.:/5 (319 download)

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Book Synopsis Proceedings by :

Download or read book Proceedings written by and published by American Society of Civil Engineers. This book was released on 1993 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Object-Based Image Analysis

Author : Thomas Blaschke
Publisher : Springer Science & Business Media
ISBN 13 : 3540770585
Total Pages : 804 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Object-Based Image Analysis by : Thomas Blaschke

Download or read book Object-Based Image Analysis written by Thomas Blaschke and published by Springer Science & Business Media. This book was released on 2008-08-09 with total page 804 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a collection of invited interdisciplinary persp- tives on the recent topic of Object-based Image Analysis (OBIA). Its c- st tent is based on select papers from the 1 OBIA International Conference held in Salzburg in July 2006, and is enriched by several invited chapters. All submissions have passed through a blind peer-review process resulting in what we believe is a timely volume of the highest scientific, theoretical and technical standards. The concept of OBIA first gained widespread interest within the GIScience (Geographic Information Science) community circa 2000, with the advent of the first commercial software for what was then termed ‘obje- oriented image analysis’. However, it is widely agreed that OBIA builds on older segmentation, edge-detection and classification concepts that have been used in remote sensing image analysis for several decades. Nevert- less, its emergence has provided a new critical bridge to spatial concepts applied in multiscale landscape analysis, Geographic Information Systems (GIS) and the synergy between image-objects and their radiometric char- teristics and analyses in Earth Observation data (EO).

Machine Learning in Image Analysis and Pattern Recognition

Author : Munish Kumar
Publisher : MDPI
ISBN 13 : 3036517146
Total Pages : 112 pages
Book Rating : 4.0/5 (365 download)

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Book Synopsis Machine Learning in Image Analysis and Pattern Recognition by : Munish Kumar

Download or read book Machine Learning in Image Analysis and Pattern Recognition written by Munish Kumar and published by MDPI. This book was released on 2021-09-08 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition.

Integrated Spatial Databases: Digital Images and GIS

Author : Peggy Agouris
Publisher : Springer Science & Business Media
ISBN 13 : 3540669310
Total Pages : 319 pages
Book Rating : 4.5/5 (46 download)

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Book Synopsis Integrated Spatial Databases: Digital Images and GIS by : Peggy Agouris

Download or read book Integrated Spatial Databases: Digital Images and GIS written by Peggy Agouris and published by Springer Science & Business Media. This book was released on 1999-12-15 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Integrated Databases, Digital Images and GIS, ISD'99, held in Portland, Maine, USA in June 1999. The 18 revised full papers presented went through a double reviewing process and were selected from nearly 40 original submissions. The book is divided into parts on object extraction from raster images, geospatial analysis, formalisms and modeling, and data access.

Sparse Coding And Its Applications In Computer Vision

Author : Zhaowen Wang
Publisher : World Scientific
ISBN 13 : 9814725064
Total Pages : 239 pages
Book Rating : 4.8/5 (147 download)

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Book Synopsis Sparse Coding And Its Applications In Computer Vision by : Zhaowen Wang

Download or read book Sparse Coding And Its Applications In Computer Vision written by Zhaowen Wang and published by World Scientific. This book was released on 2015-10-28 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broader introduction to the theories and applications of sparse coding techniques in computer vision research. It introduces sparse coding in the context of representation learning, illustrates the fundamental concepts, and summarizes the most active research directions. A variety of applications of sparse coding are discussed, ranging from low-level image processing tasks such as super-resolution and de-blurring to high-level semantic understanding tasks such as image recognition, clustering and fusion.The book is suitable to be used as an introductory overview to this field, with its theoretical part being both easy and precious enough for quick understanding. It is also of great value to experienced researchers as it offers new perspective to the underlying mechanism of sparse coding, and points out potential future directions for different applications.