Impact of Method Noise on SAR Image Despeckling

Introduction

In SAR images, the reduction of speckle noise is one of the major problems since it makes the interpretation of linear features, edges, homogeneous and heterogeneous areas difficult. On the basis of multiplicative speckle noise model, there are many despeckling techniques for noise reduction. In order to illuminate the earth’s surface, SAR uses microwave emission. Since SAR delivers its own illumination, it chokes some basic problems related to traditional passive remote sensing. Due to the ability of SAR of self-illumination, it can operate easily in day or night. It is not even affected by cloud cover or variation in solar illumination. SAR has the ability to partially penetrate the arid and hyper-arid surfaces but cannot penetrate through the stable water, hence reflect the outward action of oceans, lakes, and other water bodies and also reveals the subsurface structures of the earth (Frost et al., 1982). SAR images are formed by the consistent interaction of the emitted microwave radiation with target areas. This consistent interaction originates arbitrary constructive and destructive noisiness resulting in multiplicative kind of noise known as speckle noise all over the image. The granular pattern usually found in the SAR image either during image acquisition or due to arbitrary constructive or destructive interference is called as speckle noise. The consistent interaction of high-frequency radar waves with a complex set of scatterers is possibly the least implicit and restrictive aspect of SAR processing system design and application.

The removal of speckle noise from SAR image is called as SAR image despeckling. The scope of improvement in the despeckling field is a never-ending. There is always a chance of improvement in this domain. Since speckle noise is multiplicative in nature, so its effect on the image is far adverse than any other kind of noise. This is due to its multiplicative nature. Eliminating speckle noise is challenging task. It distorts the edges, corners, and lines of the image and also degrades the homogeneous region of the image with its granular pattern. The Bayesian approaches in the transform domain and non-Bayesian approaches are quite effective in the despeckling process than the Bayesian approaches in the spatial domain. Simple applying any single layered approach is not sufficient for better results in the despeckling process. The concept of method noise was introduced in the past decade, but its application has been widely utilized in the last couple of years. The major applications are seen in the digital images as well as in the medical images, but this concept is still untouched in the field of SAR image despeckling. Author has proposed a despeckling scheme (Singh & Shree, 2017) in 2017 using method noise thresholding and explained its major advantages. This paper is totally grounded on explaining the advantageous impact of method noise in SAR image despeckling. The concept of method noise is totally grounded on the residual image which is obtained by the subtraction operation. Let A is the noisy image and say denoising scheme XYZ is applied to it and resultant denoised image B. Then operation (A - B) is performed. The resultant is a residual image which is called the method noise. It contains the unprocessed components of the noisy image which is later processed/ filtered using any denoising scheme. It is a simple and highly effective scheme.

The paper discusses the concept, use, and implementation of the method noise in the SAR image despeckling process. The effectiveness and strength of method noise are validated by applying this concept to 14 traditional and non-traditional methods with and without method noise. The experimental results are presented using enhanced Bayesian shrinkage rule using DWT with and without method noise. The results are assessed using visual appearance and quantitative analysis (PSNR and SSIM) of the despeckled image.

The rest article is divided into three sections: Section II presents the major related work to the method noise. Section III describes the concept; use of method noise and enhanced Bayesian Shrinkage rule using DWT for the qualitative and quantitative performance of despeckled image using method noise. Section IV presents the numerical experiments with test results and discussion. The last section concludes the article.