Hyperspectral Imagery Target Detection Using Principal Component Analysis 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 Hyperspectral Imagery Target Detection Using Principal Component Analysis PDF full book. Access full book title Hyperspectral Imagery Target Detection Using Principal Component Analysis by . Download full books in PDF and EPUB format.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 100
Get Book Here
Book Description
The purpose of this research was to improve on the outlier detection methods used in hyperspectral imagery analysis. An algorithm was developed based on Principal Component Analysis (PCA), a classical multivariate technique usually used for data reduction. Using PCA, a score is computed and a test statistic is then used to make outlier declarations. First, four separate PCA test statistics were compared in the algorithm. It was found that Mahalanobis distance performed the best. This test statistic was then compared using the entire data set and a clustered data set. Since it has been shown in the literature that even one outlier can distort the covariance matrix, an iterative approach to the clustered based algorithm was developed. After each iteration, if an outlier(s) is identified, the observation(s) is removed and the algorithm is reapplied. Once no new outliers are identified or one of the stopping conditions is met, the algorithm is reapplied a final time with the new covariance matrix applied to the original data set. Experiments were designed and analyzed using analysis of variance to identify the significant factors and optimal settings to maximize each algorithm?s performance.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 100
Get Book Here
Book Description
The purpose of this research was to improve on the outlier detection methods used in hyperspectral imagery analysis. An algorithm was developed based on Principal Component Analysis (PCA), a classical multivariate technique usually used for data reduction. Using PCA, a score is computed and a test statistic is then used to make outlier declarations. First, four separate PCA test statistics were compared in the algorithm. It was found that Mahalanobis distance performed the best. This test statistic was then compared using the entire data set and a clustered data set. Since it has been shown in the literature that even one outlier can distort the covariance matrix, an iterative approach to the clustered based algorithm was developed. After each iteration, if an outlier(s) is identified, the observation(s) is removed and the algorithm is reapplied. Once no new outliers are identified or one of the stopping conditions is met, the algorithm is reapplied a final time with the new covariance matrix applied to the original data set. Experiments were designed and analyzed using analysis of variance to identify the significant factors and optimal settings to maximize each algorithm?s performance.
Author: Robert Koo
Publisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 244
Get Book Here
Book Description
Author: Yuri P. Taitano
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 174
Get Book Here
Book Description
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:
Publisher: Elsevier
ISBN: 0444639780
Category : Science
Languages : en
Pages : 802
Get Book Here
Book Description
Hyperspectral Imaging, Volume 32, presents a comprehensive exploration of the different analytical methodologies applied on hyperspectral imaging and a state-of-the-art analysis of applications in different scientific and industrial areas. This book presents, for the first time, a comprehensive collection of the main multivariate algorithms used for hyperspectral image analysis in different fields of application. The benefits, drawbacks and suitability of each are fully discussed, along with examples of their application. Users will find state-of-the art information on the machinery for hyperspectral image acquisition, along with a critical assessment of the usage of hyperspectral imaging in diverse scientific fields. - Provides a comprehensive roadmap of hyperspectral image analysis, with benefits and considerations for each method discussed - Covers state-of-the-art applications in different scientific fields - Discusses the implementation of hyperspectral devices in different environments
Author: Hans Grahn
Publisher: John Wiley & Sons
ISBN: 9780470010877
Category : Science
Languages : en
Pages : 390
Get Book Here
Book Description
Techniques and Applications of Hyperspectral Image Analysis gives an introduction to the field of image analysis using hyperspectral techniques, and includes definitions and instrument descriptions. Other imaging topics that are covered are segmentation, regression and classification. The book discusses how high quality images of large data files can be structured and archived. Imaging techniques also demand accurate calibration, and are covered in sections about multivariate calibration techniques. The book explains the most important instruments for hyperspectral imaging in more technical detail. A number of applications from medical and chemical imaging are presented and there is an emphasis on data analysis including modeling, data visualization, model testing and statistical interpretation.
Author: Chein-I Chang
Publisher: John Wiley & Sons
ISBN: 047012461X
Category : Science
Languages : en
Pages : 442
Get Book Here
Book Description
Authored by a panel of experts in the field, this book focuses on hyperspectral image analysis, systems, and applications. With discussion of application-based projects and case studies, this professional reference will bring you up-to-date on this pervasive technology, wether you are working in the military and defense fields, or in remote sensing technology, geoscience, or agriculture.
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: Anil Meerasa Cheriyadat
Publisher:
ISBN:
Category : Image analysis
Languages : en
Pages :
Get Book Here
Book Description
It is a popular practice in the remote-sensing community to apply principal component analysis (PCA) on a higher-dimensional feature space to achieve dimensionality-reduction. Several factors that have led to the popularity of PCA include its simplicity, ease of use, availability as part of popular remote-sensing packages, and optimal nature in terms of mean square error. These advantages have prompted the remote-sensing research community to overlook many limitations of PCA when used as a dimensionality-reduction tool for classification and target-detection applications. This thesis addresses the limitations of PCA when used as a dimensionality-reduction technique for extracting discriminating features from hyperspectral data. Theoretical and experimental analyses are presented to demonstrate that PCA is not necessarily an appropriate feature-extraction method for high-dimensional data when the objective is classification or target-recognition. The influence of certain data-distribution characteristics, such as within-class covariance, between-class covariance, and correlation on PCA transformation, is analyzed in this thesis. The classification accuracies obtained using PCA features are compared to accuracies obtained using other feature-extraction methods like variants of Karhunen-Loève transform and greedy search algorithms on spectral and wavelet domains. Experimental analyses are conducted for both two-class and multi-class cases. The classification accuracies obtained from higher-order PCA components are compared to the classification accuracies of features extracted from different regions of the spectrum. The comparative study done on the classification accuracies that are obtained using above feature-extraction methods, ascertain that PCA may not be an appropriate tool for dimensionality-reduction of certain hyperspectral data-distributions, when the objective is classification or target-recognition.
Author:
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 804
Get Book Here
Book Description
