Anomaly Detection in Hyperspectral Imagery Using Stable Distribution 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 Anomaly Detection in Hyperspectral Imagery Using Stable Distribution PDF full book. Access full book title Anomaly Detection in Hyperspectral Imagery Using Stable Distribution by Suat Mercan. Download full books in PDF and EPUB format.
Author: Suat Mercan
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 86
Get Book Here
Book Description
Author: Suat Mercan
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 86
Get Book Here
Book Description
Author: Patrick C. Hytla
Publisher:
ISBN:
Category : Imaging systems
Languages : en
Pages : 230
Get Book Here
Book Description
Author: Chein-I Chang
Publisher: Springer Science & Business Media
ISBN: 1441991700
Category : Computers
Languages : en
Pages : 372
Get Book Here
Book Description
Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.
Author: Francis M. Mindrup
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 246
Get Book Here
Book Description
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: Amanda K. Ziemann
Publisher:
ISBN:
Category : Machine learning
Languages : en
Pages : 328
Get Book Here
Book Description
"Imagery collected from airborne platforms and satellites provide an important medium for remotely analyzing the content in a scene. In particular, the ability to detect a specific material within a scene is of high importance to both civilian and defense applications. This may include identifying "targets" such as vehicles, buildings, or boats. Sensors that process hyperspectral images provide the high-dimensional spectral information necessary to perform such analyses. However, for a d-dimensional hyperspectral image, it is typical for the data to inherently occupy an m-dimensional space, with m “ d. In the remote sensing community, this has led to a recent increase in the use of manifold learning, which aims to characterize the embedded lower-dimensional, non-linear manifold upon which the hyperspectral data inherently lie. Classic hyperspectral data models include statistical, linear subspace, and linear mixture models, but these can place restrictive assumptions on the distribution of the data; this is particularly true when implementing traditional target detection approaches, and the limitations of these models are well-documented. With manifold learning based approaches, the only assumption is that the data reside on an underlying manifold that can be discretely modeled by a graph. The research presented here focuses on the use of graph theory and manifold learning in hyperspectral imagery. Early work explored various graph-building techniques with application to the background model of the Topological Anomaly Detection (TAD) algorithm, which is a graph theory based approach to anomaly detection. This led towards a focus on target detection, and in the development of a specific graph-based model of the data and subsequent dimensionality reduction using manifold learning. An adaptive graph is built on the data, and then used to implement an adaptive version of locally linear embedding (LLE). We artificially induce a target manifold and incorporate it into the adaptive LLE transformation; the artificial target manifold helps to guide the separation of the target data from the background data in the new, lower-dimensional manifold coordinates. Then, target detection is performed in the manifold space."--Abstract.
Author: Ariel Schlamm
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 219
Get Book Here
Book Description
"Widely used methods of target, anomaly, and change detection when applied to spectral imagery provide less than desirable results due to the complex nature of the data. In the case of hyperspectral data, dimension reduction techniques are employed to reduce the amount of data used in the detection algorithms in order to produce 'better' results and/or decreased computation time. this essentially ignores a significant amount of the data collected in k unique spectral bands. Methods presented in this work explore using the distribution of the collected data in the full k dimensions in order to identify regions of interest contained in spatial tiles of the scene. Here, interest is defined as small and large scale manmade activity. The algorithms developed in this research are primarily data driven with a limited number of assumptions. These algorithms will individually be applied to spatial subsets or tiles of the full scene to indicate the amount of interest contained. Each tile is put through a series of tests using the algorithms based on the full distribution of the data in the hyperspace. the scores from each test will be combined in such a way that each tile is labeled as either 'interesting or 'not interesting.' This provides a cueing mechanism for image analysts to visually inspect locations within a hyperspectral scenes with a high likelihood of containing manmade activity."--Abstract.
Author: Kelly R. Bush
Publisher:
ISBN:
Category : Factorization (Mathematics)
Languages : en
Pages : 92
Get Book Here
Book Description
Author: Choongyeun Cho
Publisher:
ISBN:
Category :
Languages : en
Pages : 158
Get Book Here
Book Description
(Cont.) Hyperspectral anomalies dealt with in this thesis are (1) cloud impact in hyperspectral radiance fields, (2) noisy channels and (3) scan-line miscalibration. Estimation of the cloud impact using the proposed algorithm is especially successful and comparable to an alternative physics-based algorithm. Noisy channels and miscalibrated scan-lines are also fairly well compensated or removed using the proposed algorithm.
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.
