基于偏微分方程图像去噪算法的SAR图像变化检测研究

发布时间：2017-12-27 19:23

本文关键词：基于偏微分方程图像去噪算法的SAR图像变化检测研究　出处：《新疆大学》2017年硕士论文　论文类型：学位论文

【摘要】：遥感图像变化检测是指通过分析两幅在同一场景、不同时刻采集的遥感图像,从中提取地物目标在一段时间内发生的变化的过程。随着全球生态环境的恶化、城市化的进程的加快、地震、干旱、洪涝等自然灾害的频发,快速准确地获取关键的变化信息,对于人类的生存与发展变得尤为重要。相对于其他遥感技术,合成孔径雷达(Synthetic Aperture Radar,SAR)可以全天时、全气候采集信息,可以识别伪装,穿透覆盖物,因此SAR图像变化检测被广泛地应用于森林植被覆盖面积的变化、资源探测、城市规划布局、地震等自然灾害的评估等众多领域。但是SAR图像在获取和传输过程中会被引入大量的噪声,给SAR图像变化检测带来了很大的挑战,研究SAR图像变化检测算法具有很大的现实意义。本文的研究目的是进一步提高SAR图像变化检测的检测准确度和检测效率,研究内容主要围绕如何减少噪声对图像变化检测的影响、如何构造能准确反映图像间的真实变化的差异图像以及如何选用合适图像分类方法等方面而展开,研究内容总结如下:(1)提出了一种基于P-M扩散方程的SAR图像检测算法。根据对数变换可以把图像中的乘性斑点噪声转化成加性噪声的特性,我们首先对图像进行对数变换,然后利用P-M扩散方程的去噪模型滤除图像中的噪声,再利用对数域差值法获取差异图像,最后利用FCM聚类算法对差异图像进行分类,提取变化区域。实验证明,该算法能够有效地减少乘性斑点噪声对变化检测结果的影响,提高变化检测的准确率,缩短变化检测时间。(2)提出了一种基于自适应全变分(Total Variation,TV)去噪算法的SAR图像变化检测。首先把图像转换到对数变换域,接着利用自适应全变分去噪模型对图像进行去噪。在该算法中,针对原始TV去噪算法的不足,我们采用广义能量函数,并引入一个差异变量来自动选取去噪所需要的迭代次数,避免人工选取迭代次数带来的去噪不充分或者浪费时间的问题。最后使用k-means聚类算法对差异图像进行聚类。实验结果表明,自适应全变分去噪算法能够有效地抑制图像噪声,提高算法的检测精度和检测效率。
[Abstract]:Remote sensing image change detection is the process of extracting the change of objects in a period of time by analyzing two remote sensing images collected at the same scene and time. With the deterioration of the global ecological environment, the acceleration of urbanization, the frequent occurrence of natural disasters such as earthquakes, droughts and floods, it is particularly important for human survival and development to get the key change information quickly and accurately. Compared with other remote sensing technology, synthetic aperture radar (Synthetic Aperture, Radar, SAR), the climate can all day long to collect information, can be identified through camouflage, covering many fields, so the SAR image change detection is widely used in forest vegetation cover change, resource exploration, city planning, earthquake and other natural disasters assessment etc.. However, SAR images will be introduced into the process of acquisition and transmission, which will bring a lot of noise. It will bring great challenges to SAR image change detection. It is of great practical significance to study SAR image change detection algorithm. The purpose of this study is to further improve the detection SAR image change detection accuracy and detection efficiency, the research content mainly focuses on how to reduce the impact of noise on the image, change detection and how to construct the image differences reflect the changes between images and how to select the appropriate image classification methods, the research contents are summarized as follows: (1) proposed a SAR detection algorithm of P-M images based on diffusion equation. According to the logarithmic transformation to the multiplicative speckle noise of the image is transformed into the properties of additive noise, we first compare the logarithmic transformation of the image, and then use the P-M diffusion equation to model the noise noise in the filtered image, then get the difference image using the logarithmic difference method, finally using FCM clustering algorithm to classify the image differences. Changes in the region extraction. Experiments show that the algorithm can effectively reduce the influence of multiplicative speckle noise on change detection results, improve the accuracy of change detection and shorten the detection time. (2) a kind of SAR image change detection based on adaptive total variation (Total Variation, TV) denoising algorithm is proposed. First, the image is converted to the logarithm transform domain, and then the adaptive total variation denoising model is used to denoise the image. In the algorithm, aiming at the shortage of the original TV denoising algorithm, we use generalized energy function and introduce a differential variable to automatically select the number of iterations needed for denoising, and avoid the problem of noise reduction or waste of time caused by manual selection of iterations. Finally, the K-means clustering algorithm is used to cluster the difference images. The experimental results show that the adaptive total variation denoising algorithm can effectively suppress the image noise, and improve the detection precision and efficiency of the algorithm.
【学位授予单位】：新疆大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN957.52

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