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Texture motifs in remote sensed imagery.

Newsam, Shawn Donald.

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Texture motifs in remote sensed imagery.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Texture motifs in remote sensed imagery./
Author:	Newsam, Shawn Donald.
Description:	141 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-01, Section: B, page: 0366.
Contained By:	Dissertation Abstracts International65-01B.
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3120370

Texture motifs in remote sensed imagery.
Newsam, Shawn Donald.

Texture motifs in remote sensed imagery. - 141 p.

Source: Dissertation Abstracts International, Volume: 65-01, Section: B, page: 0366.

Thesis (Ph.D.)--University of California, Santa Barbara, 2004.

This dissertation explores the use of texture for modelling geospatial objects in remote sensed images. Texture based analysis of remote sensed images has not enjoyed the same success as spectral based analysis. This is surprising since texture can be considered as context at the pixel level, and context is acknowledged to be important for analyzing geographic data. This work extends texture based analysis beyond land cover classification, which can be viewed as the current state-of-the-art, to modelling objects composed of multiple characteristic textures or texture motifs. A statistical pattern recognition framework is adopted in which the texture motifs are modelled as mixtures of Gaussians in the high dimensional texture feature space, and the model parameters are learned in an unsupervised manner using the expectation maximization algorithm. The objects and their motifs are assumed to occur at arbitrary orientations in the images which presents a formidable challenge to using orientation selective texture features. The proposed approach overcomes this challenge by exploiting the structure of texture features extracted using banks of Gabor filters tuned at different scales and orientations.Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Texture motifs in remote sensed imagery.
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035 $a (UnM)AAI3120370
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100 1 $a Newsam, Shawn Donald. $3 1900498
245 1 0 $a Texture motifs in remote sensed imagery.
300 $a 141 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-01, Section: B, page: 0366.
500 $a Chair: B. S. Manjunath.
502 $a Thesis (Ph.D.)--University of California, Santa Barbara, 2004.
520 $a This dissertation explores the use of texture for modelling geospatial objects in remote sensed images. Texture based analysis of remote sensed images has not enjoyed the same success as spectral based analysis. This is surprising since texture can be considered as context at the pixel level, and context is acknowledged to be important for analyzing geographic data. This work extends texture based analysis beyond land cover classification, which can be viewed as the current state-of-the-art, to modelling objects composed of multiple characteristic textures or texture motifs. A statistical pattern recognition framework is adopted in which the texture motifs are modelled as mixtures of Gaussians in the high dimensional texture feature space, and the model parameters are learned in an unsupervised manner using the expectation maximization algorithm. The objects and their motifs are assumed to occur at arbitrary orientations in the images which presents a formidable challenge to using orientation selective texture features. The proposed approach overcomes this challenge by exploiting the structure of texture features extracted using banks of Gabor filters tuned at different scales and orientations.
590 $a School code: 0035.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Remote Sensing. $3 1018559
690 $a 0544
690 $a 0799
710 2 0 $a University of California, Santa Barbara. $3 1017586
773 0 $t Dissertation Abstracts International $g 65-01B.
790 1 0 $a Manjunath, B. S., $e advisor
790 $a 0035
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3120370