Dilated Residual Shrinkage Network for SAR Image Despeckling

Nie, Lin; Gao, Chen; Zhou, Qingfeng; Liu, Chanzi

doi:10.1109/icsip52628.2021.9689024

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…Hence, we utilize the deep residual shrinkage network (DRSN) [66] to focus on capturing local features. The DRSN has been applied to image denoising with good performance [67], [68]. It embeds soft thresholds as a trainable shrinkage function into the CNN to force unimportant features to zero, thereby weakening the adverse effect of noise and learning more discriminative local features.…”

Section: Input Embeddingmentioning

confidence: 99%

Dual Attention-Based Global-Local Feature Extraction Network for Unsupervised Change Detection in PolSAR Images

Xu,

Li,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Due to the interference of multiplicative speckles, it is challenging to accurately detect changes in polarimetric synthetic aperture radar (PolSAR) images. Convolutional neural network (CNN) has been proven to learn rich local features from PolSAR data. However, convolution kernels with limited receptive fields have difficulty in exploring global information. Here, a dual-attention-based global-local feature extraction network (DA-GLN) is developed for unsupervised PolSAR image change detection (CD). First, we use fuzzy C-means clustering on the enhanced Shannon entropy difference image to automatically generate pseudo-labeled samples required for unsupervised CD. Subsequently, our DA-GLN utilizes a deep residual shrinkage network (DRSN) that incorporates channel attention mechanisms and soft-thresholding to weaken the influence of speckle noise and capture local features. Meanwhile, a pooling-based vision transformer (PiT) is adopted in DA-GLN to extract global features, which introduces pooling layers to complete self-attention spatial information interaction with higher efficiency than the visual transformer (ViT). Furthermore, a global-local constraint feature fusion (GLCFF) strategy is designed to effectively fuse local and global features. Finally, we employ a feature constraint-focal loss (FC-F loss) function including feature constraint loss and focal loss as the objective function of DA-GLN. Specifically, the feature constraint loss function is constructed to eliminate feature redundancy and fully exploit the complementarity between features, while the focal loss function is introduced to balance the impact of the inequality between changed and unchanged samples on the network. Numerical experiments on five real spaceborne PolSAR datasets demonstrate that our DA-GLN is more competitive than other state-of-the-art methods.

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Section: Input Embeddingmentioning

confidence: 99%

Dual Attention-Based Global-Local Feature Extraction Network for Unsupervised Change Detection in PolSAR Images

Xu,

Li,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Figure 7a demonstrates that the backbone of the encoder part is based on a deep residual shrinkage network (DRSN), which has been demonstrated to be applicable to the process of highly noised images by several studies [56][57][58][59]. The DRSN in this study was rebuilt from the ResNet-18.…”

Section: Proposed Drs-unetmentioning

confidence: 99%

DRs-UNet: A Deep Semantic Segmentation Network for the Recognition of Active Landslides from InSAR Imagery in the Three Rivers Region of the Qinghai–Tibet Plateau

et al. 2022

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At present, Synthetic Aperture Radar Interferometry (InSAR) has been an important technique for active landslides recognition in the geological survey field. However, the traditional interpretation method through human–computer interaction highly relies on expert experience, which is time-consuming and subjective. To solve the problem, this study designed an end-to-end semantic segmentation network, called deep residual shrinkage U-Net (DRs-UNet), to automatically extract potential active landslides in InSAR imagery. The proposed model was inspired by the structure of U-Net and adopted a residual shrinkage building unit (RSBU) as the feature extraction block in its encoder part. The method of this study has three main advantages: (1) The RSBU in the encoder part incorporated with soft thresholding can reduce the influence of noise from InSAR images. (2) The residual connection of the RSBU makes the training of the network easier and accelerates the convergency process. (3) The feature fusion of the corresponding layers between the encoder and decoder effectively improves the classification accuracy. Two widely used networks, U-Net and SegNet, were trained under the same experiment environment to compare with the proposed method. The experiment results in the test set show that our method achieved the best performance; specifically, the F1 score is 1.48% and 4.1% higher than U-Net and SegNet, which indicates a better balance between precision and recall. Additionally, our method has the best IoU score of over 90%. Furthermore, we applied our network to a test area located in Zhongxinrong County along Jinsha River where landslides are highly evolved. The quantitative evaluation results prove that our method is effective for the automatic recognition of potential active landslide hazards from InSAR imagery.

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“…44 evaluated experimentally the influence of training set design on the effectiveness of SAR image despeckling. For SAR image despeckling, 45 presents a dilated residual shrinkage network. A main network and a shrinking subnetwork are part of the suggested technique.…”

Section: Literature Surveymentioning

confidence: 99%

Review on nontraditional perspectives of synthetic aperture radar image despeckling

2022

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Synthetic aperture radar (SAR) image despeckling is a preprocessing method. SAR images are, by default, noisy in nature. The kind of noise found in SAR images is called speckle noise. The effect of this noise on SAR images is highly adverse. It degrades the quality of the SAR image, resulting in the loss of vital information. Since SAR images are inherently speckled in nature, it costs a lot of information loss. The removal of such noise from the SAR image is mandatory and is the first step. The elimination of speckle noise from SAR images is called SAR image despeckling. There are various traditional and nontraditional methods of SAR image despeckling based on Bayesian and non-Bayesian techniques. The SAR image despeckling methods based on Bayesian techniques are further subdivided into spatial and transform domains. This paper presents a comparative review of nontraditional perspectives on SAR image despeckling. The comparison is made based on methodology, objectives, merits, and demerits. Its focus is to do analysis of all the latest research done in the field of SAR image despeckling using nontraditional methods.

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Dilated Residual Shrinkage Network for SAR Image Despeckling

Cited by 4 publications

References 12 publications

Dual Attention-Based Global-Local Feature Extraction Network for Unsupervised Change Detection in PolSAR Images

Dual Attention-Based Global-Local Feature Extraction Network for Unsupervised Change Detection in PolSAR Images

DRs-UNet: A Deep Semantic Segmentation Network for the Recognition of Active Landslides from InSAR Imagery in the Three Rivers Region of the Qinghai–Tibet Plateau

Review on nontraditional perspectives of synthetic aperture radar image despeckling

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