ABSTRACT

The importance of Satellite Aperture Radar (SAR) imagery systems is increasing day-by-day in various field such as earth observation, hi-technology war mechanisms, etc. The images captured by SAR imagery systems are mainly used to detect and classify objects captured in the images. Due to the complexity of the image capturing process, SAR images can be highly noisy – often consisting of multiplicative noise, also known as speckle. To detect or classify objects in SAR images, speckle noise must be removed from images. During despeckling process, the preservation of important information in the SAR images while removing noise such as edge or patterns is a crucial task. Despeckling methods are liable to compromise on edge preservation ability while aspiring for good quality denoised images. Many researchers have proposed wavelet transform based despeckling approaches such as Discrete Wavelet Transform (DWT), Undecimated Wavelet Transform (UDWT), Dual Tree Complex Wavelet Transform (DTCWT). In these approaches, finding the best values of the coefficients plays an important role in yielding excellent denoised images with preserved edges. In this paper, we have proposed a novel optimization framework that optimizes thresholding coefficients of DTCWT despeckling method for SAR images. The proposed optimization framework is based on Fruit Fly Optimization (FOA) algorithm. The approach is a multi-objective optimization algorithm that is used to find maximum values for Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM) and Equivalent number of look (ENL). The maximum value of MSSIM indicates high edge preservation capacity, whereas maximum PSNR value indicates good quality denoising of images. The maximum ENL value represents a good speckle-noise smoothing capability. We have applied our framework over some classical images as well as over SAR images of MSTAR dataset. In our experiments, we found that our proposed framework results in the excellent PSNR 36.87 dB, 35.4 dB and 37.8 dB, MSSIM values 0.92, 0.93 and 0.92, respectively, in the case of lena image, MSTAR dataset image and TerraSAR-X dataset image.

摘要

卫星孔径雷达（SAR）影像系统在诸如地球观测，高科技战争机制等各个领域中的重要性日益提高。SAR影像系统捕获的图像主要用于检测和分类捕获的物体在图像中。由于图像捕获过程的复杂性，SAR图像可能会非常嘈杂，通常包含乘法噪声（也称为斑点）。要检测或分类SAR图像中的对象，必须从图像中去除斑点噪声。在去斑点过程中，在去除边缘或图案等噪声的同时，在SAR图像中保存重要信息是一项至关重要的任务。去斑点方法在追求高质量去噪图像的同时易于损害边缘保留能力。许多研究人员提出了基于小波变换的去斑点方法，例如离散小波变换（DWT），未抽取小波变换（UDWT），双树复数小波变换（DTCWT）。在这些方法中，找到最佳的系数值在产生保留边缘的出色去噪图像中起着重要作用。在本文中，我们提出了一种新颖的优化框架，该框架优化了用于SAR图像的DTCWT去斑方法的阈值系数。所提出的优化框架是基于果蝇优化（FOA）算法的。该方法是一种多目标优化算法，用于查找峰值信噪比（PSNR），平均结构相似性指数（MSSIM）和等效外观数（ENL）的最大值。MSSIM的最大值表示较高的边缘保留能力，而PSNR的最大值表示图像的高质量降噪。最大ENL值表示良好的斑点噪声平滑能力。我们已经将我们的框架应用于一些经典图像以及MSTAR数据集的SAR图像。在我们的实验中，我们发现，在lena图像，MSTAR数据集图像和TerraSAR-X数据集图像的情况下，我们提出的框架可产生出色的PSNR 36.87 dB，35.4 dB和37.8 dB，MSSIM值分别为0.92、0.93和0.92。 。