Abstract
Despeckling is a longstanding topic in synthetic aperture radar (SAR) images. Recently, many convolutional neural network (CNN) based methods have been proposed and shown state-of-the-art performance for SAR despeckling problem. However, these CNN based methods always need many training data or can only deal with specific noise level. To solve these problems, we directly embed an efficient CNN pre-trained model for additive white Gaussian noise (AWGN) with Multi-channel Logarithm with Gaussian denoising (MuLoG) algorithm to deal with the multiplicative noise in SAR images. This flexible pre-trained CNN model takes the noise level as input, thus only a single pre-trained model is needed to deal with different noise levels. We also use a detector to find the homogeneous region automatically to estimate the noise level of image as input. Embedded with MuLoG, our proposed filter can despeckle not only single channel but also multi-channel SAR images. Finally, both simulated and real (Pol)SAR images were tested in experiments, and the results show that the proposed method has better and more robust performance than others.
Highlights
Synthetic aperture radar (SAR) is a widely used technique for earth observation due to its all-weather and day-and-night imaging acquisition, providing large scale and high resolution reflectivity image of the Earth surface and cloud-penetrating capabilities [1]
Numerical experiments were conducted using by pre-trained fast and flexible denoising convolutional neural network (FFDNet) models for SAR images
The proposed methods Homo-FFDNet and Multi-channel Logarithm with Gaussian denoising (MuLoG)-FFDNet were compared to the homomorphic method with block-matching and 3D filtering (Homo-block matching 3D filter (BM3D)) [29], MuLoG method with BM3D denoiser (MuLoG-BM3D) [29], homomorphic method with pre-trained denoising convolutional neural network (DnCNN) model (Homo-DnCNN) [36], and MuLoG method with pre-trained DnCNN model (MuLoG-DNCNN) [36]
Summary
Synthetic aperture radar (SAR) is a widely used technique for earth observation due to its all-weather and day-and-night imaging acquisition, providing large scale and high resolution reflectivity image of the Earth surface and cloud-penetrating capabilities [1]. The existence of speckle increases the difficulty of SAR image processing and leads to a severe decrease of the performance for scene interpretation, such as classification and object detection [3]. To solve this problem, many despeckling techniques for SAR images have been proposed over the last three decades, but there is still a pressing need for new methods that can efficiently eliminate speckle without sacrificing the spatial resolution [4]. The Γ-MAP filter [9] assumed the intensity and speckle followed a Gamma distribution to solve a maximum a posteriori (MAP) optimization problem This filter can despeckle the noise while preserving the edges well. The window size has great influence on filter’s behaviour, which is the common failing of other spatial domain filters [3,10]
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