scholarly journals An Effective Hybrid Algorithm for De-Speckling the Speckle Noise in SAR Images

2019 ◽  
Vol 9 (1S) ◽  
pp. 252-255
Keyword(s):  
Image Processing ◽  
Synthetic Aperture Radar ◽  
Noise Reduction ◽  
Unscented Kalman Filter ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Flood Prediction ◽  
Multiplicative Noises ◽  
Aperture Radar

Image Processing has emerged as an essential lookup domain in circuit branches for quite a few decades. SAR is a type of Radar. It is connected to pics of articles like scene. It applies e SAR receiving wide development to the target for acquiring better spatial resolution than what is got through customary beam-scanning radars. In digital image processing field, processing Synthetic Aperture Radar is unexpectedly gaining focus. Very similar to all image processing methods, issues such as edge detection, enhancement and noise reduction are vital lookup troubles in SAR images also. Of late, speckle noise has emerged as a massive issue. Because of its presence, the SAR Images are classified as robust and multiplicative noises. Effecting speckle noise reduction in Synthetic Aperture Radar photographs is pretty challenging. Synthetic Aperture radar and its description is used in the utility of Flood prediction mapping. In this paper we implemented a better methodology for de-speckling Synthetic Aperture Radar imagery by way of using Fuzzy Discontinuity adaptive Non neighborhood potential filter. Application of fuzzy strategy to Importance sampling unscented kalman filter produces better end result than compared with fuzzy frost filter and also the TMAV and ATMAV produces higher end result therefore it can be a satisfactory filter for de-speckling.

scholarly journals RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2919 ◽  
Author(s):  
Agnieszka Chojka ◽  
Piotr Artiemjew ◽  
Jacek Rapiński
Keyword(s):  
Image Processing ◽  
Synthetic Aperture Radar ◽  
Nearest Neighbor ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Sar Images ◽  
Permanent Scatterers ◽  
Level 1 ◽  
Processing Techniques ◽  
Aperture Radar

Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potential to support the automatic Permanent Scatterers InSAR (PSInSAR) processing workflow during which faulty input data can lead to misinterpretation of the final outcomes. To address this issue, an efficient methodology was developed to mark images with RFI artefacts and as a consequence remove them from the stack of Synthetic Aperture Radar (SAR) images required in the PSInSAR processing workflow to calculate the ground displacements. Techniques presented in this paper for the purpose of RFI detection are based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering. As the reference classifier, a convolutional neural network was used.

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 A New Synthetic Aperture Radar (SAR) Imaging Method Combining Match Filter Imaging and Image Edge Enhancement

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4133 ◽  
Author(s):  
Bing Sun ◽  
Chuying Fang ◽  
Hailun Xu ◽  
Anqi Gao
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Imaging Method ◽  
Sar Image ◽  
Sar Images ◽  
Gradient Operator ◽  
Sar Imaging ◽  
Aperture Radar

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

scholarly journals Deep Multi-Scale Recurrent Network for Synthetic Aperture Radar Images Despeckling

2019 ◽  
Vol 11 (21) ◽  
pp. 2462 ◽  
Author(s):  
Yuanyuan Zhou ◽  
Jun Shi ◽  
Xiaqing Yang ◽  
Chen Wang ◽  
Durga Kumar ◽  
...  
Keyword(s):  
Image Processing ◽  
Synthetic Aperture Radar ◽  
Recurrent Network ◽  
Optical Image ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Processing Methods ◽  
Multi Scale ◽  
Optical Image Processing ◽  
Aperture Radar

For the existence of speckles, many standard optical image processing methods, such as classification, segmentation, and registration, are restricted to synthetic aperture radar (SAR) images. In this work, an end-to-end deep multi-scale recurrent network (MSR-net) for SAR image despeckling is proposed. The multi-scale recurrent and weights sharing strategies are introduced to increase network capacity without multiplying the number of weights parameters. A convolutional long short-term memory (convLSTM) unit is embedded to capture useful information and helps with despeckling across scales. Meanwhile, the sub-pixel unit is utilized to improve the network efficiency. Besides, two criteria, edge feature keep ratio (EFKR) and feature point keep ratio (FPKR), are proposed to evaluate the performance of despeckling capacity for SAR, which can assess the retention ability of the despeckling algorithm to edge and feature information more effectively. Experimental results show that our proposed network can remove speckle noise while preserving the edge and texture information of images with low computational costs, especially in the low signal noise ratio scenarios. The peak signal to noise ratio (PSNR) of MSR-net can outperform traditional despeckling methods SAR-BM3D (Block-Matching and 3D filtering) by more than 2 dB for the simulated image. Furthermore, the adaptability of optical image processing methods to real SAR images can be enhanced after despeckling.

Extraction of River Planforms from Synthetic Aperture Radar Imagery using Superpixel Classification

2020 ◽  
Author(s):  
Odysseas Pappas ◽  
Byron Adams ◽  
Nantheera Anantrasirichai ◽  
Alin Achim
Keyword(s):  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Textural Properties ◽  
Weather Conditions ◽  
Synthetic Aperture ◽  
Agglomerative Clustering ◽  
Resource Monitoring ◽  
Sar Images ◽  
Flood Prediction ◽  
Aperture Radar

<p>Algorithms for the detection and extraction of river planforms from remotely sensed images are of great interest to numerous applications including land planning, water resource monitoring, and flood prediction. Synthetic Aperture Radar (SAR) is a very promising modality for river monitoring and analysis as it can provide high resolution imagery regardless of weather conditions and the day/night cycle.</p><p>In this work we present an algorithm for the detection and segmentation of rivers in SAR images, with emphasis on accurate riverbank extraction. The algorithm utilises a novel superpixel segmentation algorithm that segments the image into perceptually uniform clusters of pixels based on a modelling of the SAR data with the Generalised Gamma Distribution.</p><p>The generated superpixels adhere to the edges of objects in the image (such as riverbanks) with great accuracy. Superpixels are then characterised according to several features that describe their statistical and textural properties which allows for the discrimination between river- and land-cover superpixels. The river-forming superpixels are then grouped together using unsupervised agglomerative clustering to produce river planform masks.</p><p>We demonstrate our proposed method on high resolution SAR images from the SENTINEL-1 and ICEYE platforms. Future work will focus on incorporating more complex heuristics for the identification of false positives and to circumvent apparent river discontinuities (e.g. bridges), as well as on the release of a toolbox providing open access to the geosciences community.</p>

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.

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