Despeckling of synthetic aperture radar images

Abstract

Synthetic Aperture Radar (SAR) is an active imaging technique based on the transmission and backscattering of microwave signals. SAR imagery is commonly obtained through spaceborne systems and has many applications in areas such as topography, forestry and earthquake monitoring. When measuring the intensity of the scattered signals, different objects in the captured area produce unique scatters and in cases of high surface roughness (in comparison to the signal wavelength), coherent scatters create random constructive and destructive interference, which results in an undesirable noise known as speckle. This type of noise significantly degrades the quality of SAR images, which hinders the performance of analysis tasks such as classification and segmentation. This study first investigates the statistical characteristics of speckle noise, and compares the effects of commonly used pre-processing techniques on raw SAR data. To do this, Sentinel-1 images are collected from a range of dates, and then temporally averaged to get a clean estimate. By taking the ratio image between the noisy and clean images, the noise signal is extracted and the analysis is carried out. This fundamental understanding of the noise model is then used to generate synthetic speckle, and apply it to natural images to create a suitable dataset. A series of machine learning models are trained on the pairs of clean and noisy data, with a focus on integrating features from Convolutional Neural Networks (CNNs), Denoising Autoencoders (AEs) and residual neural networks. To evaluate the models’ performance, reference-based image quality metrics such as the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) are used, for images with synthetically generated noise. In cases where the ground truth is not available, metrics that are more common in remote sensing like the Equivalent Number of Looks (ENL) and the Mean of Ratio (MoR) are used instead. The models with the best performance are analysed further, and compared with modern despeckling implementations. From the chosen test set of 102 synthetically noisy images, the best trained model obtained a notable average PSNR of 26.3502 dB and an average SSIM of 0.9034. This performance also translates to the chosen SAR images, where decent smoothing and detail preservation were observed.