Stripe noise removal in high resolution satellite remote sensing images based on deep learning

GAO Haobo; BU Tong; LI Xin; LU Shidong; ZHONG Huimin; CUI Lin

doi:10.11834/jrs.20221054

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Stripe noise removal in high resolution satellite remote sensing images based on deep learning
Vol. 27, Issue 3, Pages: 610-622(2023)
Received：20 February 2021，

Published：07 March 2023
DOI： 10.11834/jrs.20221054
稿件说明：

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高浩博，卜桐，李欣，陆世东，钟慧敏，崔林.2023.基于深度学习的高分辨率卫星遥感影像条带噪声去除.遥感学报，27（3）： 610-622 DOI： 10.11834/jrs.20221054.

Gao H B，Bu T，Li X，Lu S D，Zhong H M and Cui L. 2023. Stripe noise removal in high resolution satellite remote sensing images based on deep learning. National Remote Sensing Bulletin， 27（3）：610-622 DOI： 10.11834/jrs.20221054.

摘要

受到成像环境、硬件条件等因素的限制，高分辨率卫星遥感影像上普遍存在条带噪声的现象，其严重影响了影像的辐射质量和可用性。本文针对传统条带去除方法存在的适应性差、去噪效率低、依靠先验知识等不足，提出了一种基于深度学习卷积神经网络的条带噪声去除方法。本方法首先利用不同尺度的卷积层进行特征提取，然后对多尺度的特征图进行特征融合得到去噪底图，通过残差学习的方法在底图上预测存在的噪声分量，最后用噪声影像减去条带噪声分量实现噪声的去除。以模拟和真实获取的噪声影像为实验数据，将本文提出的方法与一些经典的去噪方法进行实验结果对比分析，实验结果表明本文提出的基于深度学习的条带噪声去除方法能够在保留影像地物细节的情况下，能以优异的速度达到最高的定量指标和最好的视觉效果，充分证明了本文方法的优越性。

Abstract

Affected by imaging conditions

data transmission

and other factors

stripe noise is common in satellite remote sensing images. It seriously restricts the quality and further use of images. In early studies

various denoising methods

such as statistics-based methods

filtering-based methods

and optimization-based methods

have been proposed to overcome the above problems. These proposed methods have achieved inspiring results in some aspects. However

they still suffer from poor adaptability

low denoising efficiency

and the need for prior knowledge. Therefore

stripe noise removal remains a challenging task.

In this study

we take advantage of the convolutional deep network while considering the characteristics of the stripe noise image itself. A deep-learning-based method is proposed

which includes three parts: a feature extraction module

a feature fusion module

and a stripe denoising module. The feature extraction module uses the convolutional layer of the same channel with different strides to extract features. As a result

different-scale feature maps of the noisy image are obtained for the following feature fusion module. The feature fusion module upsamples different-scale feature maps. It fuses these upsampled feature maps through the element-wise addition method. Finally

a denoising network is used to predict the components of stripe noise. The stripe component is subtracted from the noise image based on predictions. Given the difficulties in obtaining real noise samples

the network is trained by simulation samples. Then

it is extended to denoise real images.

Experiments on simulation and real images show the excellent performance of our network. In the quantitative assessment

the PSNR and the SSIM of our network when simulated images are used are higher than those of the four methods. In the visual assessment

our network performs well on homogeneous and nonhomogeneous objects. Our network denoises more efficiently and retains more details of ground features than traditional methods and other denoising networks. In real noise images

our method achieves the best denoising performance with the highest ICV and the lowest MRD. Compared with traditional methods

our network has a fast denoising speed

approximately 100 times faster than the denoising speed of the optimization-based denoising method. The above experimental results demonstrate that our network has the best denoising performance in simulated and real images.

In this study

a convolutional neural network denoising method based on multiscale feature fusion is proposed based on the fully convolutional neural network. The method uses residual learning to predict the strip-noise components on images. It achieves clean images by subtracting the strip components from noisy images. Experiments demonstrate that compared with traditional methods

deep learning denoising methods are adaptive for removing stripe noise of different intensities without losing image details. The strategy of feature fusion and residual learning can effectively improve the training speed and denoising accuracy of the network. In the future

skip-connected and batch normalization layers will be included to optimize training speed and improve denoising performance. Further studies will be conducted in terms of the transfer ability of the network and the extension of its application in other types of remote sensing images

such as aerial images and hyperspectral images.

关键词

Keywords

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