Automatic Target Detection in Hyperspectral Imagery Using One-dimensional MACH and EMACH Filters PDF Download
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Author: Muhammad Faysal Islam
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 140
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Book Description
Author: Muhammad Faysal Islam
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 140
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Book Description
Author: H. Hanna Tran Haskett
Publisher:
ISBN:
Category :
Languages : en
Pages : 280
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Book Description
Author: Shao-Shan Chiang
Publisher:
ISBN:
Category : Remote-sensing images
Languages : en
Pages : 258
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Book Description
Author: Firooz Sadjadi
Publisher: Springer Science & Business Media
ISBN: 0387369430
Category : Science
Languages : en
Pages : 269
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Book Description
This book examines the roles of sensors, physics–based attributes, classification methods, and performance evaluation in automatic target recognition. It details target classification from small mine–like objects to large tactical vehicles. Also explored in the book are invariants of sensor and transmission transformations, which are crucial in the development of low latency and computationally manageable automatic target recognition systems.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 15
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Book Description
Due to recent advances in hyperspectral imaging sensors many subtle unknown signal sources that cannot be resolved by multispectral sensors can be now uncovered for target detection, discrimination, and identification. Because the information about such sources is generally not available, automatic target recognition (ATR) presents a great challenge to hyperspectral image analysts. Many approaches developed for ATR are based on second-order statistics in the past years. This paper investigates ATR techniques using high order statistics. For ATR in hyperspectral imagery, most interesting targets usually occur with low probabilities and small population and they generally cannot be described by second-order statistics. Under such circumstances, using high-order statistics to perform target detection have been shown by experiments in this paper to be more effective than using second order statistics. In order to further address a challenging issue in determining the number of signal sources needed to be detected, a recently developed concept of virtual dimensionality (VD) is used to estimate this number. The experiments demonstrate that using high-order statistics-based techniques in conjunction with the VD to perform ATR are indeed very effective.
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 312
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Book Description
"Education, arts and social sciences, natural and technical sciences in the United States and Canada".
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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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: Kelly D. Friesen
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 178
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Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 389
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Book Description
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: 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.
