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

机译:空间和多时相视觉变化检测及其在SAR图像分析中的应用。

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摘要

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.;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.;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.
机译:每天生成数千个高分辨率图像。检测和分析这些图像中的变化是理解图像的关键步骤。这项工作着重于空间和多时相视觉变化检测及其在多时相合成孔径雷达(SAR)图像中的应用。; Canny边缘检测器是最广泛使用的边缘检测算法之一,由于其在SNR方面的优越性能和边缘定位,并且只有一个对单个边缘的响应。在这项工作中,我们提出了一种与原始帧级Canny算法相比,在块级上实现Canny算法的机制,而不会在边缘检测性能上造成任何损失的机制。最终的基于块的算法显着减少了内存需求,并且可以显着减少等待时间。此外,所提出的算法可以容易地与其他基于块的图像处理系统集成。此外,定量评估和主观测试表明,该算法的边缘检测性能优于原始的基于帧的算法,尤其是在图像中存在噪声的情况下。;在用于地球监测的多时间SAR图像中在应用程序中,一个关键问题是检测自然或人为灾难后发生的变化。在这项工作中,我们提出了一种新颖的相似性度量,用于使用在不同时间获取的一对SAR图像进行自动变化检测,并将其应用于空间域和小波域。此度量基于两个日期之间图像的局部统计量的演变。本地统计数据以高斯混合模型(GMM)建模,它更适合于灵活地估计具有不同土地覆盖类型的SAR图像的本地分布。对真实数据集的测试表明,就相似图的质量(使用接收器工作特性(ROC)曲线进行评估)以及最终变化检测图的总错误率而言,拟议的检测器优于现有方法。此外,我们提出了一种基于除法归一化变换的自动变化检测新相似度度量,以降低计算复杂度。测试表明,与以前建议的方法相比,我们提出的基于DNT的变化检测器在展现出竞争性检测性能的同时,实现了更低的计算复杂度。

著录项

  • 作者

    Xu, Qian.;

  • 作者单位

    Arizona State University.;

  • 授予单位 Arizona State University.;
  • 学科 Electrical engineering.;Computer science.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 134 p.
  • 总页数 134
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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