Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 15
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.
Automatic Target Recognition for Hyperspectral Imagery Using High-Order Statistics
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 15
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.
Publisher:
ISBN:
Category :
Languages : en
Pages : 15
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.
Automatic Target Recognition for Hyperspectral Imagery
Author: Kelly D. Friesen
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 178
Book Description
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 178
Book Description
Spectral-spatial Automatic Target Detection of Small Targets Using Hyperspectral Imagery
Author: H. Hanna Tran Haskett
Publisher:
ISBN:
Category :
Languages : en
Pages : 280
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 280
Book Description
Automatic Target Recognition in Hyperspectral Images Using Wavelet Decomposition and Neural Ldentifier
Author: 蘇鴻昇
Publisher:
ISBN:
Category :
Languages : en
Pages : 78
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 78
Book Description
Automatic Target Detection and Classification for Hyperspectral Imagery
Author: Shao-Shan Chiang
Publisher:
ISBN:
Category : Remote-sensing images
Languages : en
Pages : 258
Book Description
Publisher:
ISBN:
Category : Remote-sensing images
Languages : en
Pages : 258
Book Description
Automatic Target Detection in Hyperspectral Imagery Using One-dimensional MACH and EMACH Filters
Author: Muhammad Faysal Islam
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 140
Book Description
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 140
Book Description
Aided/Automatic Target Detection Using Reflective Hyperspectral Imagery for Airborne Applications
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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.
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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.
Physics of Automatic Target Recognition
Author: Firooz Sadjadi
Publisher: Springer Science & Business Media
ISBN: 0387369430
Category : Science
Languages : en
Pages : 269
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.
Publisher: Springer Science & Business Media
ISBN: 0387369430
Category : Science
Languages : en
Pages : 269
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.
High Performance Computing in Remote Sensing
Author: Antonio J. Plaza
Publisher: CRC Press
ISBN: 1420011618
Category : Computers
Languages : en
Pages : 494
Book Description
Solutions for Time-Critical Remote Sensing Applications The recent use of latest-generation sensors in airborne and satellite platforms is producing a nearly continual stream of high-dimensional data, which, in turn, is creating new processing challenges. To address the computational requirements of time-critical applications, researchers
Publisher: CRC Press
ISBN: 1420011618
Category : Computers
Languages : en
Pages : 494
Book Description
Solutions for Time-Critical Remote Sensing Applications The recent use of latest-generation sensors in airborne and satellite platforms is producing a nearly continual stream of high-dimensional data, which, in turn, is creating new processing challenges. To address the computational requirements of time-critical applications, researchers
Automatic Target Recognition
Author:
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 438
Book Description
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 438
Book Description