导　　师： 李洪平

学科专业： H1203

授予学位： 硕士

作　　者： ;

机构地区： 中国海洋大学

摘　　要： 合成孔径雷达/(SAR/)是一种主动式的微波成像雷达,由于其全天候、全天时、高分辨率、大面积的特点,成为观测海岸线变化的重要技术手段。目前,已发射了许多高分辨率的雷达卫星,其中我国发射了3颗,大量SAR海洋图像有待分类和应用,因此,从SAR图像中自动提取海岸线特征信息是一个研究热点。但是,在Speckle噪声以及分辨率因素的影响下,获得的海岸线的连续性和准确性存在一定的问题,自动化进行海岸线提取尚有困难。对此,本文提出了一种利用平稳小波变换在SAR图像中进行边缘信息提取的方法,该方法把小波分析与图像处理技术结合起来对SAR图像进行分析与处理,并且可以有效抑制Speckle噪声。 本文的主要研究内容分为如下3部分： /(1/)SAR图像海岸线特征识别的预处理：首先,采用了10*10窗口操作对SAR图像进行压缩,在不影响海岸线边缘特征的同时降低了图像处理的复杂度；接着,对SAR图像未成像黑色边界实现自动的检测与去除；然后,采用直方图均衡化和灰度拉伸方法对SAR图像进行对比度调节,提高了SAR海岸线图像的对比度。实验表明,这种方法不但改善了图像对比度而且减少了后续处理的计算量,并初步抑制了部分噪声的干扰,起到了对SAR图像滤波的作用。 /(2/)SAR图像Speckle噪声抑制：在对传统的均值与中值滤波、局域统计自适应滤波、小波阈值去噪方法分析与总结的基础上,针对本论文SAR图像海岸线边缘特征提取的特点,提出了一种改进的小波低通Speckle噪声抑制方法。该方法利用了小波变换多尺度分析特性,结合了局域统计自适应滤波算法中的增强Lee滤波的优势,实现了对SAR图像噪声抑制的目的。从实验结果来看,改进后的方法通过去除小波变换中的高频信息,保留低频信息,实现了对SAR图像Speckle噪声的有效去除。 /(3/)一种基于平稳小波变换的SAR图像海岸线边缘特征自动提取方法：首先利用平稳小波变换/(SWT/)对SAR海岸线图像进行处理,计算SWT系数的小波梯度信息/(WGI/),然后采用模极大值搜索方法进行海岸线边缘特征提取,并通过形态学细化等后处理方法对海岸线特征提取结果进行精细化处理,提高海岸线特征检测的准确性。采用本文所述的方法对ENVISAT-1卫星SAR海岸线图像进行实验与分析,结果表明平稳小波变换是一种有效的SAR图像海岸线特征检测工具。 Synthetic Aperture Radar /(SAR/), as an active microware imaging radar, is an effective tool to observe the coastline with its characteristics of all-weather, all-time, high resolution and wide coverage. So far, lots of SAR satellites have been launched and three of them belong to our country. This results huge amount of SAR data needed to be classified and processed, especially in coastline monitoring and detection. Due to the image noise and low resolution factors, it is difficult to automatically detect and classify coastline in SAR images. In this thesis, a coastline detection method based on Stationary Wavelet Transform /(SWT/) is developed, and wavelet analysis and some image processing algorithms are combined to analyze the available SAR images data. This accurate coastline feature detection method can also reduce the Speckle noise. In this thesis, three parts are discussed and stated as follows. /(1/) SAR coastline images preprocessing:Firstly, we use 10*10 pixel size window to compress SAR images aiming to reduce the image processing complexity without affecting the coastline features; Secondly, we detect and remove the black boundary lines or areas automatically to avoid the false line features; Thirdly, we apply gray level histogram equalization method and contrast stretching method to enhance the coastline images gray contrast. Experiments show that this method not only improves image gray contrast but also reduces post-processing computation cost, and also limits the Speckle noise. /(2/) SAR images Speckle noise filter algorithm methods:including the traditional filter algorithm, local statistical adaptive filter algorithm and wavelet filter algorithm. Based on this three filter methods, an improved wavelet low pass filter method is introduced. This method used the multi-scale analysis of wavelet transform features, and combined with the advantages of the enhance Lee filter in the local statistical adaptive filter algorithm to further suppress SAR images Speckle noise. From the experimental results, the improved method can effectively remove the SAR images Speckle noise, through removing high frequency information and keeping low frequency information of wavelet transform. /(3/) A coastline detection method based on Stationary Wavelet Transform in SAR images:In this method, SWT is applied to process SAR images and its coefficients are used to calculate and generate the Wavelet Gradient Information /(WGI/). The coastline detection is obtained as edges by searching the module maximum according to WGI. The morphological thinning algorithm and threshold technique are also used for edge refinement to suppress the non-coastline features such as the isolate Speckle noise. The experimental results obtained from ENVISAT-1 SAR images show that the proposed method is efficient in SAR images coastline detection.

关 键 词： 合成孔径雷达 海岸线 边缘提取 平稳小波变换 模极大值

分 类 号： [TN]

领　　域： [电子电信]