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Author: Timothy Paul Bubner
Publisher:
ISBN:
Category : Spectral analysis
Languages : en
Pages : 47
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Book Description
Author: Timothy Paul Bubner
Publisher:
ISBN:
Category : Spectral analysis
Languages : en
Pages : 47
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Book Description
Author:
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ISBN:
Category :
Languages : en
Pages : 0
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This paper presents an algorithm to support airborne, real-time automatic target detection using combined EO/IR spatial and spectral discriminants for remote sensing surveillance and reconnaissance applications. The algorithm presented in this paper is sufficiently robust and optimized to accommodate high throughput, real-time, sub-pixel, hyperspectral target detection, and can also be used to support man-in-the loop or automatic target detection. The essence of this algorithm is the ability to select the adaptive endmember spectral signatures in real-time, regardless of target, background, and system related effects such as atmospheric conditions, calibration or sensor artifacts. Based on the selected endmembers, the spectral angle of the endmembers is used as the discriminant for target detection or terrain identification. The detection performance and false alarm rate (FAR) including the performances of different combinations of individual bands will be quantified. Statistical analysis including class distributions, various moments of hyperspectral data, and the endmember spectral signatures is examined. The Forest Radiance I database is collected with the HYDICE hyperspectral sensor (reflective spectral band of 0.4um to 2.5um) at Aberdeen U.S. Army Proving Ground in Maryland. The data set covers an area of about 10 sq km.
Author:
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ISBN:
Category :
Languages : en
Pages : 7
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Imagery collected from satellites and airborne platforms provides an important tool for remotely analyzing the content of a scene. In particular, the ability to remotely detect a specific material within a scene is of critical importance in nonproliferation and other applications. The sensor systems that process hyperspectral images collect the high-dimensional spectral information necessary to perform these detection analyses. For a d-dimensional hyperspectral image, however, where d is the number of spectral bands, it is common for the data to inherently occupy an m-dimensional space with m “d. In the remote sensing community, this has led to recent interest in the use of manifold learning, which seeks to characterize the embedded lower-dimensional, nonlinear manifold that the data discretely approximate. The research presented in this paper focuses on a graph theory and manifold learning approach to target detection, using an adaptive version of locally linear embedding that is biased to separate target pixels from background pixels. Finally, this approach incorporates multiple target signatures for a particular material, accounting for the spectral variability that is often present within a solid material of interest.
Author: Prem Chandra Pandey
Publisher: Elsevier
ISBN: 0081028954
Category : Science
Languages : en
Pages : 508
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Book Description
Hyperspectral Remote Sensing: Theory and Applications offers the latest information on the techniques, advances and wide-ranging applications of hyperspectral remote sensing, such as forestry, agriculture, water resources, soil and geology, among others. The book also presents hyperspectral data integration with other sources, such as LiDAR, Multi-spectral data, and other remote sensing techniques. Researchers who use this resource will be able to understand and implement the technology and data in their respective fields. As such, it is a valuable reference for researchers and data analysts in remote sensing and Earth Observation fields and those in ecology, agriculture, hydrology and geology. - Includes the theory of hyperspectral remote sensing, along with techniques and applications across a variety of disciplines - Presents the processing, methods and techniques utilized for hyperspectral remote sensing and in-situ data collection - Provides an overview of the state-of-the-art, including algorithms, techniques and case studies
Author: Josef P. Bishoff
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages :
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"Hyperspectral imagery (HSI) has proven to be a useful tool when considering the task of target detection. Various processes have been developed that manipulate HSI data in different ways in order to render the data usable for target detection activities. A fundamental initial step in each of these processes is ensuring that the HSI data set obtained is in the same domain as the target's spectral signature. In general, remotely sensed HSI is collected in terms of digital counts which are calibrated to units of radiance, whereas spectral target signatures are normally available in units of reflectance. This work investigates target detection using simulated hyperspectral imagery captured from highly oblique angles. Specifically, this thesis seeks to determine which domain, radiance or reflectance, is more appropriate for the off-nadir case. An oblique atmospheric compensation technique based on the empirical line method (ELM) is presented and used to compensate the simulated data used in this study. The resulting reflectance cubes are subjected to a variety of standard target detection processes. A forward modeling technique that is appropriate for use on oblique hyperspectral data is also presented. This forward modeling process allows for standard target detection techniques to be applied in the radiance domain. Results obtained from the radiance and reflectance domains are comparable. Under ideal circumstances the radiance domain results observed tended to be just as good as or slightly better than results observed in the reflectance domain. These somewhat favorable results observed in the radiance domain, considered with the practicality and potential operational applicability of the forward modeling technique presented, suggest that the radiance domain is an attractive option for oblique hyperspectral target detection."--Abstract.
Author: Saurabh Prasad
Publisher: Springer Nature
ISBN: 3030386171
Category : Computers
Languages : en
Pages : 464
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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:
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ISBN:
Category :
Languages : en
Pages : 389
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This research extends the field of hyperspectral target detection by developing autonomous anomaly detection and signature matching methodologies that reduce false alarms relative to existing benchmark detectors. The proposed anomaly detection methodology adapts multivariate outlier detection algorithms for use with hyperspectral datasets containing thousands of high-dimensional spectral signatures. In so doing, the limitations of existing, non-robust anomaly detectors are identified, an autonomous clustering methodology is developed to divide an image into homogeneous background materials, and competing multivariate outlier detection methods are evaluated. To arrive at a final detection algorithm, robust parameter design methods are employed to determine parameter settings that achieve good detection performance over a range of hyperspectral images and targets. The final anomaly detection algorithm is tested against existing local and global anomaly detectors, and is shown to achieve superior detection accuracy when applied to a diverse set of hyperspectral images. The proposed signature matching methodology employs image-based atmospheric correction techniques in an automated process to transform a target reflectance signature library into a set of image signatures. This set of signatures is combined with an existing linear filter to form a target detector that is shown to perform as well or better relative to detectors that rely on complicated, information-intensive atmospheric correction schemes. The performance of the proposed methodology is assessed using a range of target materials in both woodland and desert hyperspectral scenes.
Author: Michael Theodore Eismann
Publisher: SPIE-International Society for Optical Engineering
ISBN: 9780819487872
Category : Image processing
Languages : en
Pages : 0
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Book Description
Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications that builds on the principles of material spectroscopy, radiative transfer, imaging spectrometry, and hyperspectral data processing. While there are many resources that suitably cover these areas individually and focus on specific aspects of the hyperspectral remote sensing field, this book provides a holistic treatment that captures its multidisciplinary nature. The content is oriented toward the physical principles of hyperspectral remote sensing as opposed to applications of hyperspectral technology. Readers can expect to finish the book armed with the required knowledge to understand the immense literature available in this technology area and apply their knowledge to the understanding of material spectral properties, the design of hyperspectral systems, the analysis of hyperspectral imagery, and the application of the technology to specific problems.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Changes in atmospheric conditions and sensor response for successive imaging sessions have limited the use of fixed target hyperspectral libraries, especially for multiple mission studies, to help identify and discriminate targets from cluttered backgrounds. The hyperspectral target signature instability has resulted in a dependence on anomaly detection algorithms in real time surveillance applications. These algorithms fail to meet some critical military requirements. This study examines a variety of mathematical transforms of the spectral signatures derived from missions flown on different days with starkly different weather conditions. The transforms use overlapping regions in the two data sets but avoid registering the image cubes. Some of the transforms use statistical features such as auto covariance matrices, means, and/or standard deviations of the image cubes. Other algorithms use spectral means taken from common features in the image cubes such as trees, roads, or blackbodies in both image cubes. Our study examines target spectra transformations in the long-wave infrared spectra of man-made targets and natural backgrounds obtained with the SEBASS (8-12 microns) imager as part of the Dark HORSE 2 exercise during the HYDRA data collection in November, 1998. This study computes the signal to clutter ratio (SCR) for transforms that required high accuracy registration, various spectral signature transformations that do not need any registration, and those transforms that used random, varying number of pixels in the overlap area. The transformed signatures were subsequently used in matched filter searches to successfully find targets with low false alarm rates (
Author: Joseph N. Pelton
Publisher:
ISBN: 9781461464235
Category : Artificial satellites
Languages : en
Pages :
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