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Advance in SAR Image Despeckling

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 14111

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Prof. Dr. Qingjun Zhang

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China Academy of Space Technology, Beijing Institute of Space System Engineering, Beijing 100086, China
Interests: satellite system design; microwave remote sensing technology
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Prof. Dr. Zhenfang Li

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Guest Editor

National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
Interests: SAR image processing and applications

Prof. Dr. Robert Wang

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Aerospace Information Research Institute (AIR), University of Chinese Academy of Science, Beijing, China
Interests: bistatic spaceborne SAR imaging technology; high-resolution spaceborne SAR systems and data processing
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Prof. Dr. Jian Yang

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Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Interests: radar polarimetry; feature extraction; target detection and target classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The applications of synthetic aperture radar (SAR) imaging have penetrated many fields, such as environmental monitoring, global change, disaster monitoring surface surveillance, and automatic target recognition and classification. However, speckles inevitably occur in SAR images, which are caused by the coherent superposition of a large number of randomly distributed radar echoes and have the characteristic of multiplicative noise. The speckle pattern is inherent in SAR images, which seriously deteriorates the visual effect of SAR images, increases the difficulty of SAR image interpretation and processing, and greatly restricts the reliability and effectiveness of SAR image feature extraction, target tracking, and other interpretation processing technologies. In most SAR imaging applications, speckle filtering is usually the first problem to be addressed in image interpretation. To date, many speckle suppression methods have been proposed, including spatial-domain filtering, transform-domain filtering, and deep learning methods. Better speckle suppression processing usually consists of the smooth performance of speckle noise and the retention ability of edge details.

This Special Issue provides a chance for researchers to discuss the research progress and the advanced despeckling methods. With the theme of advances in SAR image despeckling, this Special Issue covers broad topics including but not limited to the following:

Spatial-domain algorithms based on local statistics, e.g., an adaptive noise smoothing filter;
Transform-domain algorithms, e.g., wavelet filter and anisotropic diffusion filter;
Deep learning algorithms—deep learning algorithms are still in the early stage of research and need more improvement to become more general and stable;
Recent advances in speckle suppression methods.

Prof. Dr. Qingjun Zhang
Prof. Dr. Zhenfang Li
Prof. Dr. Robert Wang
Prof. Dr. Jian Yang
Guest Editors

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Keywords

synthetic aperture radar (SAR)
remote sensing
SAR despeckling
deep learning

Published Papers (12 papers)

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Research

26 pages, 1636 KiB

Open AccessArticle

DMSC-GAN: A c-GAN-Based Framework for Super-Resolution Reconstruction of SAR Images

by Yingying Kong and Si Liu

Remote Sens. 2024, 16(1), 50; https://doi.org/10.3390/rs16010050 - 21 Dec 2023

Viewed by 761

Abstract

Synthetic Aperture Radar (SAR) imagery is significant in remote sensing, but the limited spatial resolution results in restricted detail and clarity. Current super-resolution methods confront challenges such as complex network structure, insufficient sensing capability, and difficulty extracting features with local and global dependencies. To address these challenges, DMSC-GAN, a SAR image super-resolution technique based on the c-GAN framework, is introduced in this study. The design objective of DMSC-GAN is to enhance the flexibility and controllability of the model by utilizing conditional inputs to modulate the generated image features. The method uses an encoder–decoder structure to construct a generator and introduces a feature extraction module that combines convolutional operations with Deformable Multi-Head Self-Attention (DMSA). This module can efficiently capture the features of objects of various shapes and extract important background information needed to recover complex image textures. In addition, a multi-scale feature extraction pyramid layer helps to capture image details at different scales. DMSC-GAN combines perceptual loss and feature matching loss and, with the enhanced dual-scale discriminator, successfully extracts features from SAR images for high-quality super-resolution reconstruction. Extensive experiments confirm the excellent performance of DMSC-GAN, which significantly improves the spatial resolution and visual quality of SAR images. This framework demonstrates strong capabilities and potential in advancing super-resolution techniques for SAR images. Full article

(This article belongs to the Special Issue Advance in SAR Image Despeckling)

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24 pages, 7730 KiB

Open AccessArticle

Deep Learning for Integrated Speckle Reduction and Super-Resolution in Multi-Temporal SAR

by Lijing Bu, Jiayu Zhang, Zhengpeng Zhang, Yin Yang and Mingjun Deng

Remote Sens. 2024, 16(1), 18; https://doi.org/10.3390/rs16010018 - 20 Dec 2023

Viewed by 701

Abstract

In the domain of synthetic aperture radar (SAR) image processing, a prevalent issue persists wherein research predominantly focuses on single-task learning, often neglecting the concurrent impact of speckle noise and low resolution on SAR images. Currently, there are two main processing strategies. The first strategy involves conducting speckle reduction and super-resolution processing step by step. The second strategy involves performing speckle reduction as an auxiliary step, with a focus on enhancing the primary task of super-resolution processing. However, both of these strategies exhibit clear deficiencies. Nevertheless, both tasks jointly focus on two key aspects, enhancing SAR quality and restoring details. The fusion of these tasks can effectively leverage their task correlation, leading to a significant improvement in processing effectiveness. Additionally, multi-temporal SAR images covering imaging information from different time periods exhibit high correlation, providing deep learning models with a more diverse feature expression space, greatly enhancing the model’s ability to address complex issues. Therefore, this study proposes a deep learning network for integrated speckle reduction and super-resolution in multi-temporal SAR (ISSMSAR). The network aims to reduce speckle in multi-temporal SAR while significantly improving the image resolution. Specifically, it consists of two subnetworks, each taking the SAR image at time 1 and the SAR image at time 2 as inputs. Each subnetwork includes a primary feature extraction block (PFE), a high-level feature extraction block (HFE), a multi-temporal feature fusion block (FFB), and an image reconstruction block (REC). Following experiments on diverse data sources, the results demonstrate that ISSMSAR surpasses speckle reduction and super-resolution methods based on a single task in terms of both subjective perception and objective evaluation metrics regarding the quality of image restoration. Full article

(This article belongs to the Special Issue Advance in SAR Image Despeckling)

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20 pages, 17037 KiB

Open AccessArticle

Polarimetric Synthetic Aperture Radar Speckle Filter Based on Joint Similarity Measurement Criterion

by Fanyi Tang, Zhenfang Li, Qingjun Zhang, Zhiyong Suo, Zexi Zhang, Chao Xing and Huancheng Guo

Remote Sens. 2023, 15(21), 5224; https://doi.org/10.3390/rs15215224 - 03 Nov 2023

Viewed by 496

Abstract

Polarimetric Synthetic Aperture Radar (PolSAR) data is inherently characterized by speckle noise, which significantly deteriorates certain aspects of the quality of the PolSAR data processing, including the polarimetric decomposition and target interpretation. With the rapid increase in PolSAR resolution, SAR images in complex natural and artificial scenes exhibit non-homogeneous characteristics, which creates an urgent demand for high-resolution PolSAR filters. To address these issues, a new adaptive PolSAR filter based on joint similarity measure criterion (JSMC) is proposed in this paper. Firstly, a scale-adaptive filtering window is established in order to preserve the texture structure based on a multi-directional ratio edge detector. Secondly, the JSMC is proposed in order to accurately select homogeneous pixels; it describes pixel similarity based on both space distance and polarimetric distance. Thirdly, the homogeneous pixels are filtered based on statistical averaging. Finally, the airborne and spaceborne real data experiment results validate the effectiveness of our proposed method. Compared with other filters, the filter proposed in this paper provides a better outcome for PolSAR data in speckle suppression, edge texture, and the preservation of polarimetric properties. Full article

(This article belongs to the Special Issue Advance in SAR Image Despeckling)

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17 pages, 25273 KiB

Open AccessArticle

A U-Net Approach for InSAR Phase Unwrapping and Denoising

by Sachin Vijay Kumar, Xinyao Sun, Zheng Wang, Ryan Goldsbury and Irene Cheng

Remote Sens. 2023, 15(21), 5081; https://doi.org/10.3390/rs15215081 - 24 Oct 2023

Cited by 1 | Viewed by 1182

Abstract

The interferometric synthetic aperture radar (InSAR) imaging technique computes relative distances or surface maps by measuring the absolute phase differences of returned radar signals. The measured phase difference is wrapped in a 2

π

cycle due to the wave nature of light. Hence, [...] Read more.

π

cycle due to the wave nature of light. Hence, the proper multiple of 2

π

must be added back during restoration and this process is known as phase unwrapping. The noise and discontinuity present in the wrapped signals pose challenges for error-free unwrapping procedures. Separate denoising and unwrapping algorithms lead to the introduction of additional errors from excessive filtering and changes in the statistical nature of the signal. This can be avoided by joint unwrapping and denoising procedures. In recent years, research efforts have been made using deep-learning-based frameworks, which can learn the complex relationship between the wrapped phase, coherence, and amplitude images to perform better unwrapping than traditional signal processing methods. This research falls predominantly into segmentation- and regression-based unwrapping procedures. The regression-based methods have poor performance while segmentation-based frameworks, like the conventional U-Net, rely on a wrap count estimation strategy with very poor noise immunity. In this paper, we present a two-stage phase unwrapping deep neural network framework based on U-Net, which can jointly unwrap and denoise InSAR phase images. The experimental results demonstrate that our approach outperforms related work in the presence of phase noise and discontinuities with a root mean square error (RMSE) of an order of magnitude lower than the others. Our framework exhibits better noise immunity, with a low average RMSE of 0.11. Full article

(This article belongs to the Special Issue Advance in SAR Image Despeckling)

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20 pages, 8991 KiB

Open AccessArticle

Non-Local SAR Image Despeckling Based on Sparse Representation

by Houye Yang, Jindong Yu, Zhuo Li and Ze Yu

Remote Sens. 2023, 15(18), 4485; https://doi.org/10.3390/rs15184485 - 12 Sep 2023

Viewed by 1020

Abstract

Speckle noise is an inherent problem of synthetic aperture radar (SAR) images, which not only seriously affects the acquisition of SAR image information, but also greatly reduces the efficiency of image segmentation and feature classification. Therefore, research on how to effectively suppress speckle noise while preserving SAR image content information as much as possible has received increasing attention. Based on the non-local idea of SAR image block-matching three-dimensional (SAR-BM3D) algorithm and the concept of sparse representation, a novel SAR image despeckling algorithm is proposed. The new algorithm uses K-means singular value decomposition (K-SVD) to learn the dictionary to distinguish valid information and speckle noise and constructs a block filter based on K-SVD for despeckling, so as to avoid strong point diffusion problem in SAR-BM3D and achieve better speckle noise suppression with stronger adaptability. The experimental results on real SAR images show that the proposed algorithm achieves better comprehensive effect of speckle noise suppression in terms of evaluation indicators and information preservation of SAR images compared with several existing algorithms. Full article

(This article belongs to the Special Issue Advance in SAR Image Despeckling)

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23 pages, 28964 KiB

Open AccessArticle

A Multi-Channel Attention Network for SAR Interferograms Filtering Applied to TomoSAR

by Jie Li, Zhiyuan Li, Bingchen Zhang and Yirong Wu

Remote Sens. 2023, 15(18), 4401; https://doi.org/10.3390/rs15184401 - 07 Sep 2023

Viewed by 896

Abstract

Tomographic synthetic aperture radar (TomoSAR) is an advanced synthetic aperture radar (SAR) interferometric technique that can retrieve 3-D spatial information. However, the performances of 3-D reconstruction could be degraded due to the noise in interferograms, which makes the filtering crucial before the tomographic reconstruction. As known, filters for single-channel interferograms are common, but those for multi-channel interferograms are still rare. In this paper, we propose a multi-channel attention network to denoise the multi-channel interferograms applied for TomoSAR, which is built on the basis of multi-channel attention blocks. An important feature of the block is the local context mixing before the computation of attention maps across channels, which explores the intra-channel local information and the inter-channel relationship of the multi-channel interferograms. Based on this architecture, the proposed method can effectively filter the noise while preserving the structures in interferograms, thus improving the performance of tomographic reconstruction. The network is trained by simulated data and the promising results of both simulated and real data validate the effectiveness of our proposed method. Full article

(This article belongs to the Special Issue Advance in SAR Image Despeckling)

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25 pages, 14012 KiB

Open AccessArticle

Despeckling of SAR Images Using Residual Twin CNN and Multi-Resolution Attention Mechanism

by Blaž Pongrac and Dušan Gleich

Remote Sens. 2023, 15(14), 3698; https://doi.org/10.3390/rs15143698 - 24 Jul 2023

Cited by 1 | Viewed by 1110

Abstract

The despeckling of synthetic aperture radar images using two different convolutional neural network architectures is presented in this paper. The first method presents a novel Siamese convolutional neural network with a dilated convolutional network in each branch. Recently, attention mechanisms have been introduced to convolutional networks to better model and recognize features. Therefore, we propose a novel design for a convolutional neural network using an attention mechanism for an encoder–decoder-type network. The framework consists of a multiscale spatial attention network to improve the modeling of semantic information at different spatial levels and an additional attention mechanism to optimize feature propagation. Both proposed methods are different in design but they provide comparable despeckling results in subjective and objective measurements in terms of correlated speckle noise. The experimental results are evaluated on both synthetically generated speckled images and real SAR images. The methods proposed in this paper are able to despeckle SAR images and preserve SAR features. Full article

(This article belongs to the Special Issue Advance in SAR Image Despeckling)

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20 pages, 25724 KiB

Open AccessArticle

Adaptive Speckle Filter for Multi-Temporal PolSAR Image with Multi-Dimensional Information Fusion

by Haoliang Li, Xingchao Cui, Mingdian Li, Junwu Deng and Siwei Chen

Remote Sens. 2023, 15(14), 3679; https://doi.org/10.3390/rs15143679 - 23 Jul 2023

Cited by 1 | Viewed by 1064

Abstract

Polarimetric synthetic aperture radar (PolSAR) is an important sensor for earth observation. Multi-temporal PolSAR images obtained by successive observations of the region of interest contain rich polarimetric–temporal–spatial information of the land covers, which has wide applications. Speckle filtering becomes a necessary pre-processing for many subsequent applications. Currently, it is common to filter multi-temporal PolSAR data by directly using a speckle filter developed for single SAR or PolSAR data. The cross-correlation between different time series contains rich information in multi-temporal PolSAR images. How to utilize complete polarimetric–temporal–spatial information becomes a large challenge to achieve more satisfied performances of speckle reduction and details preservation simultaneously. This work dedicates to this issue and develops a novel speckle filtering approach for multi-temporal PolSAR data by multi-dimensional information fusion. The core idea is to establish an adaptive and efficient strategy of similar pixel selection based on the similarity test of multi-temporal polarimetric covariance matrices. This similar pixel selection scheme fuses the complete information of multi-temporal PolSAR data. The sensitivity of the proposed scheme is demonstrated with several typical and challenging texture patterns. Then, an adaptive speckle filter is established specifically for multi-temporal PolSAR data. Intensive comparison studies are carried out with airborne UAVSAR datasets and spaceborne ALOS/PALSAR datasets. Quantitative investigations in terms of the equivalent number of looks (ENL) and the figure of merit (FOM) indexes demonstrate and validate the superiority of the proposed method. Full article

(This article belongs to the Special Issue Advance in SAR Image Despeckling)

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21 pages, 4187 KiB

Open AccessArticle

A Priori Knowledge Based Ground Moving Target Indication Technique Applied to Distributed Spaceborne SAR System

by Bin Cai, Xiaolong Hao, Li Chen, Jia Liang, Tianhao Cheng and Ying Luo

Remote Sens. 2023, 15(9), 2467; https://doi.org/10.3390/rs15092467 - 08 May 2023

Viewed by 1090

Abstract

Through formation flying, the distributed spaceborne SAR(synthetic aperture radar) system can increase the number of spatial degree of freedoms (DOFs) and provide flexible multi-baselines for SAR-GMTI (ground moving target indication), which improves the system performance. This paper proposes an a priori knowledge-based adaptive clutter cancellation and moving target detection technique applied to the distributed spaceborne SAR-GMTI systems. Firstly, the adaptive clutter cancellation technique is exploited to suppress the ground clutter. A priori knowledge, such as road network information, is integrated to the adaptive clutter cancellation processor to reduce any moving target steering vector mismatch. Secondly, adaptive matched filter (AMF) and adaptive beamformer orthogonal rejection test (ABORT) are exploited as adaptive detection techniques for moving target detection. Due to the dense road network, the moving target steering vector estimation may be ambiguous for the different position and orientation of the roads. The multiple hypothesis testing (MHT) technique is proposed to detect the moving targets and resolve the potential ambiguities. A scheme is exploited to detect, classify, and relocate the moving targets. Finally, simulation experiments and performance analysis have demonstrated the effectiveness and robustness of the proposed technique. Full article

(This article belongs to the Special Issue Advance in SAR Image Despeckling)

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20 pages, 13297 KiB

Open AccessArticle

Guidance-Aided Triple-Adaptive Frost Filter for Speckle Suppression in the Synthetic Aperture Radar Image

by Jiamu Li, Wenbo Yu, Yi Wang, Zijian Wang, Jiarong Xiao, Zhongjun Yu and Desheng Zhang

Remote Sens. 2023, 15(3), 551; https://doi.org/10.3390/rs15030551 - 17 Jan 2023

Cited by 3 | Viewed by 1434

Abstract

Speckle noise exists inherently in the synthetic aperture radar (SAR) image. Its multiplicative property leads to lots of difficulties in SAR image processing. A novel guidance-aided triple-adaptive Frost filter is proposed in this paper, which has potential for real-time processing platforms. Firstly, a scale-adaptive sliding window sizing method is adopted to determine the neighborhood ranges for every point in the image. All the subsequent processing is based on it. Then, an adaptive calculation for the tuning factor in the Frost filter is embedded into the proposed method. Lastly, the feature information apertured from the original image is used to provide guidance for edge recovery automatically, which guarantees the satisfactory ability for feature preservation. Thus, a novel improved Frost filter is proposed with triple adaptabilities. Both the positioning accuracy and response sensitivity of the scale-adaptive sliding window sizing method are verified first. The superiority of the adaptive tuning factor combined with the scale-adaptive sliding window is confirmed by two comparison experiments. At last, the results of speckle suppression experiments on the synthetic images and two natural airborne SAR images present a better performance than other methods. Full article

(This article belongs to the Special Issue Advance in SAR Image Despeckling)

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20 pages, 10436 KiB

Open AccessCommunication

A Block-Scale FFT Filter Based on Spatial Autocorrelation Features of Speckle Noise in SAR Image

by Xigang Wang, Zhiguo Meng, Si Chen, Zhuangzhuang Feng, Xinbiao Li, Tianhao Guo, Chunmei Wang and Xingming Zheng

Remote Sens. 2023, 15(1), 247; https://doi.org/10.3390/rs15010247 - 31 Dec 2022

Cited by 4 | Viewed by 1830

Abstract

In order to reduce the impact of noise on the accuracy of inversion products based on SAR images, many filtering algorithms have been developed for noise reduction of SAR images. This paper proposes a filtering method based on the spatial autocorrelation feature of the block fast Fourier transform (BFFT). The method statistically analyses the autocorrelation length of speckle noise on Sentinel-1B images for different features and then constructs a relationship between autocorrelation length and noise period. After that, the size of the optimal FFT filtering window radius was determined based on the relationship between the noise period and the components in the image frequency domain. Finally, we filtered the SAR image within the parcels. We compared BFFT with six commonly used filtering methods. The results show that: (1) The noise periods of the soybean, corn, paddy, and water objects on the SAR image have little difference, with noise periods of 3.36, 3.17, 3.13, and 3.14 pixels on the VV polarization and 3.49, 3.17, 2.94, and 2.42 pixels on the VH polarization; (2) after the BFFT filtering in the land parcel area, the mean value of the backscattering coefficient (BC) kept constant, whilst at the same time, the standard deviation (STD) was reduced to half of that before the filtering and (3) the BFFT and NLM filtering methods have a better effect on noise reduction inside the block. The BFFT filtering method retains the variation trend between different regions within the block and preserves the block boundary’s clarity. This study provides a new idea for refined image processing. Full article

(This article belongs to the Special Issue Advance in SAR Image Despeckling)

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25 pages, 6207 KiB

Open AccessArticle

Airborne Elevation DBF-TOPS SAR/InSAR Method Based on LOS Motion Compensation and Channel Error Equalization

by Zhiyong Suo, Jingjing Ti, Hongli Xiang, Leru Zhang, Chao Xing and Tingting Wang

Remote Sens. 2022, 14(18), 4542; https://doi.org/10.3390/rs14184542 - 11 Sep 2022

Viewed by 1336

Abstract

Digital beamforming (DBF) TOPS SAR in elevation is a new synthetic aperture radar (SAR) system, which has the advantage of wide swath coverage and a high signal-to-noise ratio (SNR). In this paper, considering the phase preservation demand for interferometric SAR (InSAR) processing, the complete processing chain for DBF-TOPS SAR/InSAR in elevation is proposed with a wide beam angle and channels’ amplitude and phase errors. Firstly, we analyze the airborne motion compensation method along the line-of-sight direction for TOPS SAR with squint angle. Furthermore, for the large-range beam angle of DBF, the sub-swaths division process is presented for the range-dependent radar look angle, and the sub-swaths division criterion is also given in the analytic expression. Then, the relative amplitude and phase errors’ estimation and compensation method between channels is provided in the range frequency domain based on the pivoting filter with coherence weighting, which is convenient for DBF processing and SNR improvement. Finally, the DEMs are generated under different conditions to compare the phase preservation performance. The effectiveness of the proposed processing chain is verified with both simulated data and airborne real DBF-TOPS SAR/InSAR data. Full article

(This article belongs to the Special Issue Advance in SAR Image Despeckling)

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