國立虎尾科技大學 |

General Model-Based Decomposition Framework for Polarimetric SAR Images.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	General Model-Based Decomposition Framework for Polarimetric SAR Images./
作者:	Dauphin, Stephen.
面頁冊數:	1 online resource (113 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Mathematics. -
電子資源:	click for full text (PQDT)
ISBN:	9781369868319

General Model-Based Decomposition Framework for Polarimetric SAR Images.
Dauphin, Stephen.

General Model-Based Decomposition Framework for Polarimetric SAR Images. - 1 online resource (113 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--Colorado State University, 2017.

Includes bibliographical references

Polarimetric synthetic aperture radars emit a signal and measure the magnitude, phase, and polarization of the return. Polarimetric decompositions are used to extract physically meaningful attributes of the scatterers. Of these, model-based decompositions intend to model the measured data with canonical scatter-types. Many advances have been made to this field of model-based decomposition and this work is surveyed by the first portion of this dissertation.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369868319Subjects--Topical Terms:

527692
Mathematics.
Index Terms--Genre/Form:

554714
Electronic books.

General Model-Based Decomposition Framework for Polarimetric SAR Images.
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245 1 0 $a General Model-Based Decomposition Framework for Polarimetric SAR Images.
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300 $a 1 online resource (113 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: Margaret Cheney.
502 $a Thesis (Ph.D.)--Colorado State University, 2017.
504 $a Includes bibliographical references
520 $a Polarimetric synthetic aperture radars emit a signal and measure the magnitude, phase, and polarization of the return. Polarimetric decompositions are used to extract physically meaningful attributes of the scatterers. Of these, model-based decompositions intend to model the measured data with canonical scatter-types. Many advances have been made to this field of model-based decomposition and this work is surveyed by the first portion of this dissertation.
520 $a A general model-based decomposition framework (GMBDF) is established that can decompose polarimetric data with different scatter-types and evaluate how well those scatter-types model the data by comparing a residual term. The GMBDF solves for all the scatter-type parameters simultaneously that are within a given decomposition by minimizing the residual term. A decomposition with a lower residual term contains better scatter-type models for the given data. An example is worked through that compares two decompositions with different surface scatter-type models.
520 $a As an application of the polarimetric decomposition analysis, a novel terrain classification algorithm of polSAR images is proposed. In the algorithm, the results of state-of-the-art polarimetric decompositions are processed for an image. Pixels are then selected to represent different terrain classes. Distributions of the parameters of these selected pixels are determined for each class. Each pixel in the image is given a score according to how well its parameters fit the parameter distributions of each class. Based on this score, the pixel is either assigned to a predefined terrain class or labeled unclassified.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Mathematics. $3 527692
650 4 $a Electrical engineering. $3 596380
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710 2 $a Colorado State University. $b Mathematics. $3 1180659
773 0 $t Dissertation Abstracts International $g 78-11B(E).
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