東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Analysis of modeling, training, and ...

Farrell, Michael D., Jr.

Linked to FindBook

Google Book

Amazon

博客來

Analysis of modeling, training, and dimension reduction approaches for target detection in hyperspectral imagery.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analysis of modeling, training, and dimension reduction approaches for target detection in hyperspectral imagery./
Author:	Farrell, Michael D., Jr.
Description:	121 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6155.
Contained By:	Dissertation Abstracts International66-11B.
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3198535
ISBN:	9780542433573

Analysis of modeling, training, and dimension reduction approaches for target detection in hyperspectral imagery.
Farrell, Michael D., Jr.

Analysis of modeling, training, and dimension reduction approaches for target detection in hyperspectral imagery. - 121 p.

Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6155.

Thesis (Ph.D.)--Georgia Institute of Technology, 2005.

Whenever a new sensor or system comes online, engineers and analysts responsible for processing the measured data turn first to methods that are tried and true on existing systems. This is a natural, if not wholly logical approach, and is exactly what has happened in the advent of hyperspectral imagery (HSI) exploitation. However, a closer look at the assumptions made by the approaches published in the literature has not been undertaken.

ISBN: 9780542433573Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Analysis of modeling, training, and dimension reduction approaches for target detection in hyperspectral imagery.
LDR:03051nmm 2200313 4500 001 1824606
005 20061201083841.5
008 130610s2005 eng d
020 $a 9780542433573
035 $a (UnM)AAI3198535
035 $a AAI3198535
040 $a UnM $c UnM
100 1 $a Farrell, Michael D., Jr. $3 1913669
245 1 0 $a Analysis of modeling, training, and dimension reduction approaches for target detection in hyperspectral imagery.
300 $a 121 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6155.
500 $a Director: Russell M. Mersereau.
502 $a Thesis (Ph.D.)--Georgia Institute of Technology, 2005.
520 $a Whenever a new sensor or system comes online, engineers and analysts responsible for processing the measured data turn first to methods that are tried and true on existing systems. This is a natural, if not wholly logical approach, and is exactly what has happened in the advent of hyperspectral imagery (HSI) exploitation. However, a closer look at the assumptions made by the approaches published in the literature has not been undertaken.
520 $a This thesis analyzes three key aspects of HSI exploitation: statistical data modeling, covariance estimation from training data, and dimension reduction. These items are part of standard processing schemes, and it is worthwhile to understand and quantify the impact that various assumptions for these items have on target detectability and detection statistics.
520 $a First, the accuracy and applicability of the standard Gaussian (i.e., Normal) model is evaluated, and it is shown that the elliptically contoured t-distribution (EC-t) sometimes offers a better statistical model for HSI data. A finite mixture approach for EC- t is developed in which all parameters are estimated simultaneously without a priori information. Then the effects of making a poor covariance estimate are shown by including target samples in the training data. Multiple test cases with ground targets are explored. They show that the magnitude of the deleterious effect of covariance contamination on detection statistics depends on algorithm type and target signal characteristics. Next, the two most widely used dimension reduction approaches are tested. It is demonstrated that, in many cases, significant dimension reduction can be achieved with only a minor loss in detection performance.
520 $a In addition, a concise development of key HSI detection algorithms is presented, and the state-of-the-art in adaptive detectors is benchmarked for land mine targets. Methods for detection and identification of airborne gases using hyperspectral imagery are discussed, and this application is highlighted as an excellent opportunity for future work.
590 $a School code: 0078.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Physics, Atmospheric Science. $3 1019431
690 $a 0544
690 $a 0608
710 2 0 $a Georgia Institute of Technology. $3 696730
773 0 $t Dissertation Abstracts International $g 66-11B.
790 1 0 $a Mersereau, Russell M., $e advisor
790 $a 0078
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3198535