Nonlocal CNN SAR Image Despeckling

Cozzolino, Davide; Verdoliva, Luisa; Scarpa, Giuseppe; Poggi, Gianfranco

doi:10.3390/rs12061006

Cited by 73 publications

(48 citation statements)

References 53 publications

(75 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Only images with short temporal baseline generally offer sufficient temporal stability, but this often comes with a strong temporal correlation of speckle, which undermines the ability of the network to efficiently remove speckle. The same analysis bears for [21], where a non-local CNN is instead trained.…”

Section: Related Workmentioning

confidence: 94%

SAR2SAR: A Semi-Supervised Despeckling Algorithm for SAR Images

Dalsasso

Denis

Tupin

2021

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

View full text Add to dashboard Cite

Speckle reduction is a key step in many remote sensing applications. By strongly affecting synthetic aperture radar (SAR) images, it makes them difficult to analyse. Due to the difficulty to model the spatial correlation of speckle, a deep learning algorithm with semi-supervision is proposed in this paper: SAR2SAR. Multi-temporal time series are leveraged and the neural network learns to restore SAR images by only looking at noisy acquisitions. To this purpose, the recently proposed noise2noise framework [1] has been employed. The strategy to adapt it to SAR despeckling is presented, based on a compensation of temporal changes and a loss function adapted to the statistics of speckle.A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-theart filters. Then, results on real images are discussed, to show the potential of the proposed algorithm. The code is made available to allow testing and reproducible research in this field.

show abstract

Section: Related Workmentioning

confidence: 94%

SAR2SAR: A Semi-Supervised Despeckling Algorithm for SAR Images

Dalsasso

Denis

Tupin

2021

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

View full text Add to dashboard Cite

show abstract

“…The development of more powerful computing devices and the increase of data availability has led to substantial advances in machine learning (ML) methods. The use of ML methods allows remote sensing systems to reach high performance in many complex tasks, e.g., despecklization [66][67][68][69][70][71][72][73][74][75][76][77], object detection, semantic segmentation or image classification. These advancements are due to the capability of Deep Neural Networks to automatically learn suitable features from images in a data-driven approach, without manually setting the parameters of specific algorithms.…”

Section: Comparison With Modern Despeckling Methodsmentioning

confidence: 99%

“…Another notable CNN approach was introduced in [74], where the authors used a NLM algorithm, while the weights for pixel-wise similarity measures were assigned using a CNN. The network takes as input a patch extracted from the original domain image, and outputs a set of filter weights, adapted to the local image content.…”

Section: Comparison With Modern Despeckling Methodsmentioning

confidence: 99%

Hyperanalytic Wavelet-Based Robust Edge Detection

2021

View full text Add to dashboard Cite

The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of acquired images with a Gaussian filter. We show experimentally that this filtering is not sufficient in case of strong Additive White Gaussian or multiplicative speckle noise, because the remaining grains of noise produce false edges. The aim of this paper is to improve edge detection robustness against Gaussian and speckle noise by preceding the Canny’s edge detector with a new type of denoising system. We propose a two-stage denoising system acting in the Hyperanalytic Wavelet Transform Domain. The results obtained in applying the proposed edge detection method outperform state-of-the-art edge detection results from the literature.

show abstract

“…Finally, as the first C-band multi-polarization spaceborne SAR in China, GF-3 plays an important role in providing high-quality data for scientific experiments, therefore, it has aroused our great interest to process data from the GF-3 satellite utilizing the nonconvex and TV regularization method. Experimental results and image quality analysis verify the effectiveness and advantages of the method: compared with L 1 regularization, the method can not only improve the reconstruction accuracy but also enhance region-based features represented by the radiometric resolution; compared with the matched filtering method, the method can suppress speckles as well as sidelobes and additive noise; compared with the method of speckle removal in the image domain including multilook processing [24,25], the method can process the raw echo data and generate SAR images after speckle reduction, without sacrificing the spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Sparse SAR Imaging and Quantitative Evaluation Based on Nonconvex and TV Regularization

Zhang

Zhou

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Sparse signal processing has been used in synthetic aperture radar (SAR) imaging due to the maturity of compressed sensing theory. As a typical sparse reconstruction method, L1 regularization generally causes bias effects as well as ignoring region-based features. Our team has proposed to linearly combine the nonconvex penalty and the total variation (TV)-norm penalty as a compound regularizer in the imaging model, called nonconvex and TV regularization, which can not only reduce the bias caused by L1 regularization but also enhance point-based and region-based features. In this paper, we use the variable splitting scheme and modify the alternating direction method of multipliers (ADMM), generating a novel algorithm to solve the above optimization problem. Moreover, we analyze the radiometric properties of sparse-signal-processing-based SAR imaging results and introduce three indexes suitable for sparse SAR imaging for quantitative evaluation. In experiments, we process the Gaofen-3 (GF-3) data utilizing the proposed method, and quantitatively evaluate the reconstructed SAR image quality. Experimental results and image quality analysis verify the effectiveness of the proposed method in improving the reconstruction accuracy and the radiometric resolution without sacrificing the spatial resolution.

show abstract

Nonlocal CNN SAR Image Despeckling

Cited by 73 publications

References 53 publications

SAR2SAR: A Semi-Supervised Despeckling Algorithm for SAR Images

SAR2SAR: A Semi-Supervised Despeckling Algorithm for SAR Images

Hyperanalytic Wavelet-Based Robust Edge Detection

Sparse SAR Imaging and Quantitative Evaluation Based on Nonconvex and TV Regularization

Contact Info

Product

Resources

About