Innovative Statistical Inference for Anomaly Detection in Hyperspectral Imagery PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Innovative Statistical Inference for Anomaly Detection in Hyperspectral Imagery PDF full book. Access full book title Innovative Statistical Inference for Anomaly Detection in Hyperspectral Imagery by . Download full books in PDF and EPUB format.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
Get Book Here
Book Description
A statistical motivated idea is proposed and its application to hyperspectral imagery is presented, as a viable alternative to testing a two-sample hypothesis using conventional methods. This idea led to the design of two novel algorithms for object detection. The first algorithm, referred to as semiparametric (SemiP), is based on some of the advances made on semiparametric inference. A logistic model, based on case-control data, and its maximum likelihood method are presented, along with the analysis of its asymptotic behavior. The second algorithm, referred to as an approximation to semiparametric (AsemiP), is based on fundamental theorems from large sample theory and is designed to approximate the performance properties of the SemiP algorithm. Both algorithms have a remarkable ability to accentuate local anomalies in a scene. The AsemiP algorithm is particularly more appealing, as it replaces complicated SemiP's equations with simpler ones describing the same phenomenon. Experimental results using real hyperspectral data are presented to illustrate the effectiveness of both algorithms.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
Get Book Here
Book Description
A statistical motivated idea is proposed and its application to hyperspectral imagery is presented, as a viable alternative to testing a two-sample hypothesis using conventional methods. This idea led to the design of two novel algorithms for object detection. The first algorithm, referred to as semiparametric (SemiP), is based on some of the advances made on semiparametric inference. A logistic model, based on case-control data, and its maximum likelihood method are presented, along with the analysis of its asymptotic behavior. The second algorithm, referred to as an approximation to semiparametric (AsemiP), is based on fundamental theorems from large sample theory and is designed to approximate the performance properties of the SemiP algorithm. Both algorithms have a remarkable ability to accentuate local anomalies in a scene. The AsemiP algorithm is particularly more appealing, as it replaces complicated SemiP's equations with simpler ones describing the same phenomenon. Experimental results using real hyperspectral data are presented to illustrate the effectiveness of both algorithms.
Author: Manqi Zhao
Publisher:
ISBN:
Category :
Languages : en
Pages : 414
Get Book Here
Book Description
Abstract: Processing high dimensional data arises in a number of real world applications such as financial data analysis, hyperspectral imagery, and video surveillance. The data are organized in a rectangular array with n rows and p columns, where the rows represent different measurements and the columns represent different features. High dimensional statistical inference studies signal detection and estimation problems in the scenario when n “ p . The main challenge of high dimensional statistical inference is the curse of dimensionality phenomena. The curse of dimensionality leads to intractability of accurately approximating high-dimensional density function. Nevertheless, data samples in many high dimensional problems come from an underlying low dimensional space or manifold. This limits the degrees of freedom (DOF) in the ambient space. This structure can be exploited for statistical inference. Another feature of high dimensional data is concentration of measure phenomena, which states that certain smooth random functions in high dimensional space are nearly constant. The philosophy is that under mild conditions it is easy to predict the behavior of high dimensional data.In this thesis, we exploit the DOF structure in detection and estimation of high dimensional data together with concentration of measure inequalities to obtain new results. In particular we consider the sparsity model for compressed sensing, the joint sparse and Markov structure for blind deconvolution, the manifold model for outlier detection and the temporally local anomaly structure for time-series anomaly detection. We present a linear programming solution for signal support recovery from noisy measurements that leverages sparse constraint. We simultaneously reconstruct the unknown autoregressive filter and the driving process in light of the joint structure on sparsity and Markov property. We develop novel non-parametric adaptive anomaly detection algorithm for high dimensional data that can adapt to local sparse manifold structure. We develop a clustering algorithm that accounts for highly unbalanced proximal and complex shaped clusters based on the scheme of reweighting the graph edge similarity. We propose a new paradigm for time-series anomaly detection that exploits the local anomaly structure. Our analysis in compressed sensing shows that the achievable bound in terms of SNR, the number of measurements, and admissible sparsity level of a linear programming solution matches the optimal information-theoretic in an order-wise sense. Our result in anomaly detection suggests that estimating high dimensional level-set can be avoided by computing a sufficient p-value statistic. The resulting anomaly detector is asymptotically uniformly most powerful against any uniformly mixing density. We also provide a generalization of this p-value statistic in time-series anomaly detection with false alarm control.
Author: Elwood T. Waddell
Publisher:
ISBN:
Category :
Languages : en
Pages : 308
Get Book Here
Book Description
Author: Songyot Nakariyakul
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Saurabh Prasad
Publisher: Springer
ISBN: 9783030386160
Category : Computers
Languages : en
Pages : 466
Get Book Here
Book Description
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.
Author: Timothy J. Doster
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 172
Get Book Here
Book Description
"The topological anomaly detection (TAD) algorithm differs from other anomaly detection algorithms in that it does not rely on the data's being normally distributed. We have built on this advantage of TAD by extending the algorithm so that it gives a measure of the number of anomalous objects, rather than the number of anomalous pixels, in a hyperspectral image. We have done this by identifying and integrating clusters of anomalous pixels, which we accomplished with a graph-theoretical method that combines spatial and spectral information. By applying our method, the Anomaly Clustering algorithm, to hyperspectral images, we have found that our method integrates small clusters of anomalous pixels, such as those corresponding to rooftops, into single anomalies; this improves visualization and interpretation of objects. We have also performed a local linear embedding (LLE) analysis of the TAD results to illustrate its application as a means of grouping anomalies together. By performing the LLE algorithm on just the anomalies identified by the TAD algorithm, we drastically reduce the amount of computation needed for the computationally-heavy LLE algorithm. We also propose an application of a shifted QR algorithm to improve the speed of the LLE algorithm."--Abstract.
Author: Saurabh Prasad
Publisher: Springer Nature
ISBN: 3030386171
Category : Computers
Languages : en
Pages : 464
Get Book Here
Book Description
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.
Author: Gustavo Camps-Valls
Publisher: Springer Nature
ISBN: 3031022475
Category : Technology & Engineering
Languages : en
Pages : 242
Get Book Here
Book Description
Earth observation is the field of science concerned with the problem of monitoring and modeling the processes on the Earth surface and their interaction with the atmosphere. The Earth is continuously monitored with advanced optical and radar sensors. The images are analyzed and processed to deliver useful products to individual users, agencies and public administrations. To deal with these problems, remote sensing image processing is nowadays a mature research area, and the techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, data coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This book covers some of the fields in a comprehensive way. Table of Contents: Remote Sensing from Earth Observation Satellites / The Statistics of Remote Sensing Images / Remote Sensing Feature Selection and Extraction / Classification / Spectral Mixture Analysis / Estimation of Physical Parameters
Author: Dalton S. Rosario
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Mingxi Wu
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
With any user-supplied arbitrary likelihood function, the naive algorithm would conduct a LRT over each rectangular region in the grid, rank all of the rectangles based on the computed LRT statistics, and return the top few most interesting rectangles. To speed this process, the LRT framework uses novel and effective pruning methods to prune a large fraction of the rectangles without computing their associated LRT statistics. For all of the three research issues, extensive experiments show significant speedups comparing to the alternative algorithms on real problem over real data.
