Synthetic Aperture Radar Image Denoising Algorithm Based on Deep Learning

Xiangwei Fu; Huilin Shan; Lü Zongkui; Xingtao Wang

doi:10.3788/AOS221437

Abstract

Objective

Synthetic aperture radar (SAR) is a kind of sensor to capture microwaves. Its principle is to establish images through the reflection of waveforms, so as to solve the problem that traditional optical remote sensing radars are affected by weather, air impurities, and other environmental factors when collecting images. The most widely used SAR is change detection (CD). CD refers to the dynamic acquisition of image information from a certain target, which includes three steps: image preprocessing, generation of difference maps, and analysis and calculation of difference maps. It is applied to the estimation of natural disasters, management and allocation of resources, and measurement of land topographic characteristics. However, in the process of CD, the inherent speckle noise in SAR images will reduce the performance of CD. Therefore, the image denoising method has become a basic method of preprocessing in CD. How to restore a clean image from a noisy SAR image is an urgent problem to be solved.

Methods

Traditional denoising algorithms of SAR images generally use the global denoising idea whose principle is to use the global similar information in images to perform processing and judgment. In the case of the high resolution of images, these algorithms need a series of preprocessing such as smoothing and then complete pixel distinction through the neighborhood processing of each image block. The algorithms usually occupy huge computing resources and have certain spatial and temporal limitations in practical applications. In addition, they cannot efficiently complete the denoising task. In terms of deep learning, some algorithms perform well, but there is still room for improvement in network convergence speed, model redundancy, and accuracy. To solve these problems, this paper proposes a denoising algorithm based on a multi-scale attention cascade convolutional neural network (MALNet). The network mainly uses the idea of multi-scale irregular convolution kernel and attention. Compared with a single convolution kernel, a multi-scale irregular convolution kernel has an excellent image receptive field. In other words, it can collect image information from different scales to extract more detailed image features. Subsequently, the convolution kernels of different scales are concat layers in the network, and an attention mechanism is introduced into a concat feature map to divide the attention of the features so that the whole model has a positive enhancement ability for the main features of the image. In the middle of the network, the dense cascade layer is used to further strengthen the features. Finally, the image restoration and reconstruction are realized by network subtraction.

Results and Discussions

In this paper, qualitative and quantitative experiments are carried out to evaluate and demonstrate the performance of the proposed MALNet model in denoising. The WNNM, SAR-BM3D, and SAR-CNN algorithms are compared with our proposed method. The clear state and complete signs of the denoised images are visually observed. In order to make a fair comparison, we use the default settings of the three algorithms provided by the authors in the literatures. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and image entropy are used as objective evaluation indexes. The PSNR, SSIM, and image entropy are calculated as error metrics.

Three denoising algorithms are compared, and airpoirt, mountain, and coast are selected as verification images. The denoising effects of airport images (Fig. 7), coast images (Fig. 8), and mountain images (Fig. 9) are analyzed. It shows the visual effect comparison of denoising results of different algorithms. In Figs. 7-9, 6 figures are successively noise-free image, noise image, denoised image obtained by WNNM, denoised image obtained by SAR-BM3D, denoised image obtained by SAR-CNN and denoised image obtained by MALNet. It is obvious that the WNNM denoised image has many defects that are not removed completely, and the texture loss is quite serious. SAR-BM3D denoised image retains some details, but the aircraft fuselage is very vague, and the tail part has gotten most of the edge information erased. Although the aircraft wing in the SAR-CNN denoised image is recovered, the whole aircraft at the bottom is still far from the reference image, and the recovered small objects are blurred.

It can be seen from Table 3 that the average PSNR value of the proposed MALNet is about 9.25 dB higher than that of SAR-BM3D, about 0.75 dB higher than that of SAR-CNN, and about 14.45 dB higher than that of WNNM. Moreover, in terms of the noise level, MALNet is 0.01 dB less than that SAR-CNN. The PSNR value of the proposed MALNet model at each noise level is higher than that of other algorithms. Especially, when the noise parameter is 20, the proposed method is 2.56 dB higher than that of the SAR-CNN algorithm. In terms of structural similarity (Table 4), it can be seen that the SSIM of MALNet is mostly the highest among all methods. Only when the noise parameter is 50, it is slightly lower than that of SAR-CNN, but the average SSIM is still the highest. The average information entropy of the denoised images by the four algorithms is 7.113492 $b i t / p i x e l$ for WNNM, 6.842258 $b i t / p i x e l$ for SAR-BM3D, 7.499375 $b i t / p i x e l$ for SAR-CNN, and 6.6917 $b i t / p i x e l$ for MALNet. The proposed algorithm outperforms WNNM, SAR-BM3D, and SAR-CNN by 0.42179 $b i t / p i x e l$ , 0.15056 $b i t / p i x e l$ , and 0.80768 $b i t / p i x e l$ , respectively. Therefore, in terms of the three objective evaluation indexes of PSNR, SSIM, and image entropy, the proposed network in this paper has better denoising performance than other comparison methods.

Conclusions

In this paper, a new denoising model MALNet is proposed for solving the noise in SAR images. This model uses an end-to-end architecture and does not require separate subnets or manual intervention. The solution includes three modules, i.e., multi-scale irregular convolution module, feature extraction module based on attention mechanism, and feature enhancement module based on dense cascade network. The model also adds batch normalization and global average pooling to improve its adaptability. It can complete convergence without massive data sets. The image data can complete convergence after 150 rounds of training. The training efficiency is outstanding, and the portability is positive. The experimental results show that compared with those of other traditional image denoising algorithms, the PSNR and SSIM of the proposed algorithm are improved by 0.75 dB-14.45 dB and 0.01-0.16, respectively. The proposed algorithm is superior to other algorithms in image entropy and can better recover the details of images.