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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: Josef P. Bishoff
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages :
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Book Description
"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: Chein-I Chang
Publisher: Springer Science & Business Media
ISBN: 1441991700
Category : Computers
Languages : en
Pages : 372
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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: Muhammad Faysal Islam
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 140
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Author: H. Hanna Tran Haskett
Publisher:
ISBN:
Category :
Languages : en
Pages : 280
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Book Description
Author: Yuval Cohen
Publisher:
ISBN:
Category : Image analysis
Languages : en
Pages : 256
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Author: Amanda K. Ziemann
Publisher:
ISBN:
Category : Machine learning
Languages : en
Pages : 328
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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:
Publisher:
ISBN:
Category :
Languages : en
Pages : 19
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Book Description
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: Xiaoli Jiao
Publisher:
ISBN: 9781243759825
Category :
Languages : en
Pages : 140
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Book Description
Hyperspectral imaging has become an emerging technique in remote sensing analysis. With high spectral/spatial resolution many unknown material substances can be revealed by hyperspectral imaging sensors for data exploitation. While it significantly improved the capability for target detection it also threw great challenges in designing and developing effective methods to process data and to get the needed information for image analysis. The primary focus of this dissertation is the development of unsupervised approaches to target detection and classification for hyperspectral imagery. The research represents a significant departure from supervised approaches where the target information is assumed to be given or can be obtained a priori. Three major types of spectral targets are of particular interest in this dissertation. One is endmembers whose spectral signatures are idealistically pure. Another is anomaly which usually occurs in small size with signatures significantly different from image background. The third is human-made objects. All these types of spectral targets are usually appear in small population and occur with low probabilities, e.g., special spices in agriculture and ecology, toxic wastes in environmental monitoring, rare minerals in geology, drug/smuggler trafficking in law enforcement, combat vehicles in the battlefield, landmines in war zones, chemical/biological agents in bioterrorism, weapon concealment and mass graves. These spectral targets are generally considered as insignificant objects because of their very limited spatial information but they are actually critical and crucial for defense and intelligence analysis since they are insignificant compared to targets with large sample pools and generally hard to be identified by visual inspection. From a statistical point of view, the spectral information statistics of such special targets cannot be captured by 2nd order statistics as variances but rather by high-order statistics (HOS) as skewness, kurtosis and etc. In light of this interpretation we categorize the image pixels into two classes. One is background (BKG) with pixel spectral signature characterized by 2nd statistics. The other is target with pixel signature characterized by high-order statistics. Once image pixel vectors are categorized into BKG and target classes according to spectral properties, the follow-up task is how to find them. One is how many of them. The other is how to extract them. The key discovery that led to the techniques developed in the remaining chapters of this dissertation is the recognition that a data normalization technique called sphering can retain high order statistics but remove the 1st and 2nd order statistics of a data set. Three least-squares based unsupervised virtual endmember finding algorithms (LS-VEFA) and a component-analysis based unsupervised virtual endmember finding algorithms (CA-VEFA) are developed in this dissertation to extract target and background signatures for image analysis based on linear spectral mixture analysis (LSMA) model. In addition, to address the drawbacks of the current virtual dimensionality (VD) techniques which is originally designed to estimate the number of distinct signatures in a hyperspectral image, an orthogonal subspace projection approach has developed to find the number of signal resources in hyperspectral imagery with the implementation of two separately developed algorithms, unsupervised target sample generation (UTSG) algorithms and unsupervised background sample generation (UBSG) algorithms. The mathematical basis of these new processing approaches is the concept of subspace projection. Projection of each pixel in a...
Author: Timothy E. Smetek
Publisher:
ISBN:
Category : Anomaly detection (Computer security)
Languages : en
Pages : 744
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Author: Shao-Shan Chiang
Publisher:
ISBN:
Category : Remote-sensing images
Languages : en
Pages : 258
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