scholarly journals Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images

2021 ◽  
Vol 13 (21) ◽  
pp. 4383
Author(s):  
Gang Zhang ◽  
Zhi Li ◽  
Xuewei Li ◽  
Sitong Liu
Keyword(s):  
Synthetic Aperture Radar ◽  
Real World ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Noisy Image ◽  
Deep Convolutional Neural Networks ◽  
Sar Images ◽  
Radar Images ◽  
Image Pairs ◽  
Aperture Radar

Self-supervised method has proven to be a suitable approach for despeckling on synthetic aperture radar (SAR) images. However, most self-supervised despeckling methods are trained by noisy-noisy image pairs, which are constructed by using natural images with simulated speckle noise, time-series real-world SAR images or generative adversarial network, limiting the practicability of these methods in real-world SAR images. Therefore, in this paper, a novel self-supervised despeckling algorithm with an enhanced U-Net is proposed for real-world SAR images. Firstly, unlike previous self-supervised despeckling works, the noisy-noisy image pairs are generated from real-word SAR images through a novel generation training pairs module, which makes it possible to train deep convolutional neural networks using real-world SAR images. Secondly, an enhanced U-Net is designed to improve the feature extraction and fusion capabilities of the network. Thirdly, a self-supervised training loss function with a regularization loss is proposed to address the difference of target pixel values between neighbors on the original SAR images. Finally, visual and quantitative experiments on simulated and real-world SAR images show that the proposed algorithm notably removes speckle noise with better preserving features, which exceed several state-of-the-art despeckling methods.

scholarly journals Removing Speckle Noise in Synthetic Aperture Radar Images using Combined Intensity Coherence Vector & Hybrid Filtering

2021 ◽  
Vol 58 (1) ◽  
pp. 4289-4295
Author(s):  
Dr. D. Suresh Et al.
Keyword(s):  
Synthetic Aperture Radar ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Signal To Noise ◽  
Sar Images ◽  
Radar Images ◽  
Hybrid Filtering ◽  
Edge Based ◽  
Aperture Radar

Noise will be unavoidable in image securing practice and denoising is a fundamental advance to recoup the image quality. The image of Synthetic Aperture Radar (SAR) is intrinsically misrepresented in dot noise that happens because of coherent nature of the dispersing wonders. Denoising SAR images target eliminating dot while safeguarding image highlights, for example, surface, edges, and point targets. The blend of nonlocal gathering and changed area filtering has coordinated the cutting edge denoising methods. Notwithstanding, this methodology makes an intense suspicion that image fix itself gives a brilliant guess on the genuine boundary, which prompts predisposition issue transcendently under genuine dot noise. Another impediment is that the for the most part utilized fix pre-determination techniques can't productively avoid the exceptions and harm the edges. The SAR image is infused with spot noise, and afterward edge based marker controlled watershed division is applied to recognize the homogeneous locales in SAR image. For every locale, the local pixels are distinguished by utilizing Intensity Coherence Vector (ICV) and are denoised autonomously by utilizing a half and half filtering, which contains the improved forms of ice, middle and mean channel. The exploratory outcomes show that the proposed strategy outflanks different techniques, for example, fix based filtering, non-nearby methods, wavelets and old style dot channels in wording higher wavelets signal-to-noise and edge conservation proportions relatively.

scholarly journals Backprojection Autofocus of Large Ships with Arbitrary Motion for Synthetic Aperture Radar

2020 ◽  
Author(s):  
Aron Sommer
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Computational Costs ◽  
Radar Images ◽  
Open Sea ◽  
Sharp Image ◽  
Autofocus Algorithm ◽  
Rough Sea ◽  
Aperture Radar

Radar images of the open sea taken by airborne synthetic aperture radar (SAR) show typically several smeared ships. Due to their non-linear motions on a rough sea, these ships are smeared beyond recognition, such that their images are useless for classification or identification tasks. The ship imaging algorithm presented in this thesis consists of a fast image reconstruction using the fast factorized backprojection algorithm and an extended autofocus algorithm of large moving ships. This thesis analysis the factorization parameters of the fast factorized backprojection algorithm and describes how to choose them nearoptimally in order to reconstruct SAR images with minimal computational costs and without any loss of quality. Furthermore, this thesis shows how to estimate and compensate for the translation, the rotation and the deformation of a large arbitrarily moving ship in order to reconstruct a sharp image of the ship. The proposed autofocus technique generates images in which the ...

ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5091
Author(s):  
Jinxiao Wang ◽  
Fang Chen ◽  
Meimei Zhang ◽  
Bo Yu
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Glacial Lake ◽  
Landsat 8 ◽  
Glacial Lakes ◽  
Sar Images ◽  
Radar Images ◽  
Optical Images ◽  
Sar Data ◽  
Aperture Radar

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction.

scholarly journals On the Origin of Atmospheric Frontal Lines off the East Coast of Taiwan Observed on Spaceborne Synthetic Aperture Radar Images

2010 ◽  
Vol 138 (2) ◽  
pp. 475-496 ◽  
Author(s):  
Werner Alpers ◽  
Jen-Ping Chen ◽  
Chia-Jung Pi ◽  
I-I. Lin
Keyword(s):  
Synthetic Aperture Radar ◽  
East Coast ◽  
Physical Mechanism ◽  
Mesoscale Model ◽  
Synthetic Aperture ◽  
Katabatic Wind ◽  
Mountain Range ◽  
Sar Images ◽  
Radar Images ◽  
Aperture Radar

Abstract Frontal lines having offshore distances typically between 40 and 80 km are often visible on synthetic aperture radar (SAR) images acquired over the east coast of Taiwan by the European Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2) and Envisat. In a previous paper the authors showed that they are of atmospheric and not of oceanic origin; however, in that paper they did not give a definite answer to the question of which physical mechanism causes them. In this paper the authors present simulations carried out with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, which shows that the frontal lines are associated with a quasi-stationary low-level convergence zone generated by the dynamic interaction of onshore airflow of the synoptic-scale wind with the coastal mountain range of the island of Taiwan. Reversed airflow collides with the onshore-flowing air leading to an uplift of air, which is often accompanied by the formation of bands of increased cloud density and of rainbands. The physical mechanism causing the generation of the frontal lines is similar to the one responsible for the formation of cloud bands off the Island of Hawaii as described by Smolarkiewicz et al. Four SAR images are shown, one acquired by ERS-2 and three by Envisat, showing frontal lines at the east coast of Taiwan caused by this generation mechanism. For these events the recirculation pattern, as well as the frontal (or convective) lines observed, were reproduced quite well with the meteorological model. So, it is argued that the observed frontal lines are not seaward boundaries of (classical) barrier jets or of katabatic wind fields, which have characteristics that are quite different from the flow patterns around the east coast of Taiwan as indicated by the SAR images.

scholarly journals Multi-Hypothesis Topological Isomorphism Matching Method for Synthetic Aperture Radar Images with Large Geometric Distortion

2021 ◽  
Vol 13 (22) ◽  
pp. 4637
Author(s):  
Runzhi Jiao ◽  
Qingsong Wang ◽  
Tao Lai ◽  
Haifeng Huang
Keyword(s):  
Synthetic Aperture Radar ◽  
Stable Matching ◽  
Synthetic Aperture ◽  
Topological Isomorphism ◽  
Matching Method ◽  
Sar Images ◽  
Keypoint Detection ◽  
Radar Images ◽  
Geometric Distortions ◽  
Aperture Radar

The dramatic undulations of a mountainous terrain will introduce large geometric distortions in each Synthetic Aperture Radar (SAR) image with different look angles, resulting in a poor registration performance. To this end, this paper proposes a multi-hypothesis topological isomorphism matching method for SAR images with large geometric distortions. The method includes the Ridge-Line Keypoint Detection (RLKD) and Multi-Hypothesis Topological Isomorphism Matching (MHTIM). Firstly, based on the analysis of the ridge structure, a ridge keypoint detection module and a keypoint similarity description method are designed, which aim to quickly produce a small number of stable matching keypoint pairs under large look angle differences and large terrain undulations. The keypoint pairs are further fed into the MHTIM module. Subsequently, the MHTIM method is proposed, which uses the stability and isomorphism of the topological structure of the keypoint set under different perspectives to generate a variety of matching hypotheses, and iteratively achieves the keypoint matching. This method uses both local and global geometric relationships between two keypoints, hence it achieving better performance compared with traditional methods. We tested our approach on both simulated and real mountain SAR images with different look angles and different elevation ranges. The experimental results demonstrate the effectiveness and stable matching performance of our approach.

scholarly journals Flood Mapping in the Coastal Region of Bangladesh Using Sentinel-1 SAR Images: A Case Study of Super Cyclone Amphan

2021 ◽  
Vol 7 (3) ◽  
pp. 267
Author(s):  
Pollen Chakma ◽  
Aysha Akter
Keyword(s):  
Synthetic Aperture Radar ◽  
Coastal Region ◽  
Flood Damage ◽  
Synthetic Aperture ◽  
Flood Mapping ◽  
Sar Images ◽  
Radar Images ◽  
Super Cyclone ◽  
Aperture Radar

Floods are triggered by water overflow into drylands from several sources, including rivers, lakes, oceans, or heavy rainfall. Near real-time (NRT) flood mapping plays an important role in taking strategic measures to reduce flood damage after a flood event. There are many satellite imagery based remote sensing techniques that are widely used to generate flood maps. Synthetic aperture radar (SAR) images have proven to be more effective in flood mapping due to its high spatial resolution and cloud penetration capacity. This case study is focused on the super cyclone, commonly known as Amphan, stemming from the west Bengal-Bangladesh coast across the Sundarbans on 20 May 2020, with a wind speed between 155 -165  gusting up to 185 . The flooding extent is determined by analyzing the pre and post-event synthetic aperture radar images, using the change detection and thresholding (CDAT) method. The results showed an inundated landmass of 2146 on 22 May 2020, excluding Sundarban. However, the area became 1425 about a week after the event, precisely on 28 May 2020 . This persistency generated a more severe and intense flood, due to the broken embankments. Furthermore, 13 out of 19 coastal districts were affected by the flooding, while 8 were highly inundated, including Bagerhat, Pirojpur, Satkhira, Khulna, Barisal, Jhalokati, Patuakhali and Barguna. These findings were subsequently compared with an inundation map created with a validation survey immediately after the event and also with the disposed location using a machine learning-based image classification technique. Consequently, the comparison showed a close similarity between the inundation scenario and the flood reports from the secondary sources. This circumstance envisages the significant role of CDAT application in providing relevant information for an effective decision support system.

scholarly journals Removing Speckle Noise in Synthetic Aperture Radar Images using Linear Filters

2020 ◽  
Vol 9 (3S) ◽  
pp. 395-397
Keyword(s):  
Synthetic Aperture Radar ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Linear Filtering ◽  
Linear Filters ◽  
Radar Images ◽  
Hybrid Filtering ◽  
Hybrid Filters ◽  
Aperture Radar

Speckle is a granular aggravation, typically demonstrated as a multiplicative noise that influences Synthetic aperture radar (SAR) pictures, just as every single cognizant picture. In the course of the most recent three decades, a few techniques have been proposed for the decrease of spot, or despeckling, in SAR pictures. The examination begins with the linear filtering, non-linear filtering, adaptive filtering, and hybrid filtering. In spite of the fact that the old style straight separating strategies have lower execution similarly, the hybridization between them beats than the as of late proposed techniques. Be that as it may, the cautious determination of such filters and their impacting request exceptionally influences the presentation of such filtering methodologies. In this paper, Hybrid filtering is proposed for SAR despeckling, which involves the improved variants of frost, median and mean filters. The presentation of the proposed framework is broke down and contrasted and the as of late SAR despeckling strategies. The outcomes demonstrate that the hybrid filters are focused as they can despeckle the SAR pictures superior to the current methods

scholarly journals Despeckling Algorithms For Removing Noise In Medical Images

2021 ◽  
Vol 12 (6) ◽  
pp. 87-94
Author(s):  
D.Devasena Et.al
Keyword(s):  
Synthetic Aperture Radar ◽  
Satellite Images ◽  
Medical Images ◽  
Imaging Techniques ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Subjective Analysis ◽  
Radar Images ◽  
Different Types ◽  
Aperture Radar

The medical and satellite images are mostly corrupted by a multiplicative granular noise called speckle noise which degrades the quality of the images captured by using medical imaging techniques and also Synthetic Aperture Radar images. It causes difficulties in image interpretation and this is mainly due to back scattered signals from the multiple targets. In medical field, the diagnosis of the tissues, bones and organs takes place by using imaging techniques. By using different imaging techniques, the medical images are captured and used for diagnosis. Different types of filtering techniques are proposed in the literature to remove the speckle noise in medical and satellite images. In this research paper different types of adaptive filters and its modifications are proposed and compared. The filters like modified lee filter, modified Edge Enhanced lee filter, modified fast bilateral filter and Modified Particle Swarm Optimization based despeckling algorithm. The results are verified for both simulated images and real medical images and also for Synthetic Aperture Radar images. The results are compared in terms of both objective and subjective analysis for simulated and real medical images. The simulation is done using MATLAB R2013 and the visual qualities of the images are analyzed for varying noise densities.

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