Remote Sensing Image Denoising Based on Deep and Shallow Feature Fusion and Attention Mechanism

Optical remote sensing images are widely used in the fields of feature recognition, scene semantic segmentation, and others. However, the quality of remote sensing images is degraded due to the influence of various noises, which seriously affects the practical use of remote sensing images. As remote...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 5; p. 1243
Main Authors Han, Lintao, Zhao, Yuchen, Lv, Hengyi, Zhang, Yisa, Liu, Hailong, Bi, Guoling
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2022
Subjects
Algorithms
attention mechanism
Datasets
Deep learning
Feature extraction
feature fusion
Feature recognition
image denoising
Image processing
Image quality
Image reconstruction
Image segmentation
Machine learning
Modules
neural network
Neural networks
Noise
Noise reduction
Normal distribution
Object recognition
Random noise
Remote sensing
Semantics
Target recognition
Teaching methods
Visual perception
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Summary:Optical remote sensing images are widely used in the fields of feature recognition, scene semantic segmentation, and others. However, the quality of remote sensing images is degraded due to the influence of various noises, which seriously affects the practical use of remote sensing images. As remote sensing images have more complex texture features than ordinary images, this will lead to the previous denoising algorithm failing to achieve the desired result. Therefore, we propose a novel remote sensing image denoising network (RSIDNet) based on a deep learning approach, which mainly consists of a multi-scale feature extraction module (MFE), multiple local skip-connected enhanced attention blocks (ECA), a global feature fusion block (GFF), and a noisy image reconstruction block (NR). The combination of these modules greatly improves the model’s use of the extracted features and increases the model’s denoising capability. Extensive experiments on synthetic Gaussian noise datasets and real noise datasets have shown that RSIDNet achieves satisfactory results. RSIDNet can improve the loss of detail information in denoised images in traditional denoising methods, retaining more of the higher-frequency components, which can have performance improvements for subsequent image processing.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14051243