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Spatial and Multi-Temporal Visual Ch...

Xu, Qian.

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Spatial and Multi-Temporal Visual Change Detection with Application to SAR Image Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Spatial and Multi-Temporal Visual Change Detection with Application to SAR Image Analysis./
作者:	Xu, Qian.
面頁冊數:	134 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-04(E), Section: B.
Contained By:	Dissertation Abstracts International76-04B(E).
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3666516
ISBN:	9781321395723

Spatial and Multi-Temporal Visual Change Detection with Application to SAR Image Analysis.
Xu, Qian.

Spatial and Multi-Temporal Visual Change Detection with Application to SAR Image Analysis. - 134 p.

Source: Dissertation Abstracts International, Volume: 76-04(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2014.

This item must not be sold to any third party vendors.

Thousands of high-resolution images are generated each day. Detecting and analyzing variations in these images are key steps in image understanding. This work focuses on spatial and multitemporal visual change detection and its applications in multi-temporal synthetic aperture radar (SAR) images.

ISBN: 9781321395723Subjects--Topical Terms:

649834
Electrical engineering.

Spatial and Multi-Temporal Visual Change Detection with Application to SAR Image Analysis.
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245 1 0 $a Spatial and Multi-Temporal Visual Change Detection with Application to SAR Image Analysis.
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500 $a Source: Dissertation Abstracts International, Volume: 76-04(E), Section: B.
500 $a Adviser: Lina J. Karam.
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506 $a This item must not be sold to any third party vendors.
520 $a Thousands of high-resolution images are generated each day. Detecting and analyzing variations in these images are key steps in image understanding. This work focuses on spatial and multitemporal visual change detection and its applications in multi-temporal synthetic aperture radar (SAR) images.
520 $a The Canny edge detector is one of the most widely-used edge detection algorithms due to its superior performance in terms of SNR and edge localization and only one response to a single edge. In this work, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance as compared to the original frame-level Canny algorithm. The resulting block-based algorithm has significantly reduced memory requirements and can achieve a significantly reduced latency. Furthermore, the proposed algorithm can be easily integrated with other block-based image processing systems. In addition, quantitative evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images.
520 $a In the context of multi-temporal SAR images for earth monitoring applications, one critical issue is the detection of changes occurring after a natural or anthropic disaster. In this work, we propose a novel similarity measure for automatic change detection using a pair of SAR images acquired at different times and apply it in both the spatial and wavelet domains. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are modeled as a Gaussian Mixture Model (GMM), which is more suitable and flexible to approximate the local distribution of the SAR image with distinct land-cover typologies. Tests on real datasets show that the proposed detectors outperform existing methods in terms of the quality of the similarity maps, which are assessed using the receiver operating characteristic (ROC) curves, and in terms of the total error rates of the final change detection maps. Furthermore, we proposed a new similarity measure for automatic change detection based on a divisive normalization transform in order to reduce the computation complexity. Tests show that our proposed DNT-based change detector exhibits competitive detection performance while achieving lower computational complexity as compared to previously suggested methods.
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