A Neutral-Network-Fusion Architecture for Automatic Extraction of Oceanographic Features from Satellite Remote Sensing Imagery PDF Download
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Languages : en
Pages : 0
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This report describes an approach for automatic feature detection from fusion of remote sensing imagery using a combination of neural network architecture and the Dempster-Shafer (DS) theory of evidence. Deterministic or idealized shapes are used to characterize surface signatures of oceanic and atmospheric fronts manifested in satellite remote sensing imagery. Raw satellite images are processed by a bank of radial basis function (RBF) neural networks trained on idealized shapes. The final classification results from the fusion of the outputs of the separate RBF. The fusion mechanism is based on DS evidential reasoning theory. The approach is initially tested for detecting different features on a single sensor and extended to classifying features observed by multiple sensors.
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Languages : en
Pages : 0
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Book Description
This report describes an approach for automatic feature detection from fusion of remote sensing imagery using a combination of neural network architecture and the Dempster-Shafer (DS) theory of evidence. Deterministic or idealized shapes are used to characterize surface signatures of oceanic and atmospheric fronts manifested in satellite remote sensing imagery. Raw satellite images are processed by a bank of radial basis function (RBF) neural networks trained on idealized shapes. The final classification results from the fusion of the outputs of the separate RBF. The fusion mechanism is based on DS evidential reasoning theory. The approach is initially tested for detecting different features on a single sensor and extended to classifying features observed by multiple sensors.
Author: Farid Askari
Publisher:
ISBN:
Category : Pattern recognition systems
Languages : en
Pages : 26
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Author: Farid Askari
Publisher:
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Category : Image processing
Languages : en
Pages : 26
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Author: Kari Sentz
Publisher:
ISBN:
Category : Dempster-Shafer theory
Languages : en
Pages : 100
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Book Description
Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. Dempster-Shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise measurement from experiments, or when knowledge is obtained from expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. This report surveys a number of possible combination rules for Dempster-Shafer structures and provides examples of the implementation of these rules for discrete and interval-valued data.
Author: Xiaofeng Li
Publisher: Springer Nature
ISBN: 9811963754
Category : Science
Languages : en
Pages : 351
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Book Description
This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing.
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Category : Aeronautics
Languages : en
Pages : 980
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Category : Aeronautics
Languages : en
Pages : 704
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Author: Anil Kumar
Publisher: CRC Press
ISBN: 1000091546
Category : Computers
Languages : en
Pages : 177
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Book Description
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.
Author:
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ISBN:
Category : Electrical engineering
Languages : en
Pages : 2240
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Book Description
Author: Giulietta S. Fargion
Publisher:
ISBN:
Category :
Languages : en
Pages : 196
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Book Description
