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.

The Application of PS-InSAR Technology on Land Subsidence in Cangzhou Region

2011 ◽  
Vol 268-270 ◽  
pp. 1934-1939
Author(s):  
Kun Chao Lei ◽  
Hui Li Gong ◽  
Xiao Juan Li ◽  
Bei Bei Chen ◽  
Ji Wei Li ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Land Subsidence ◽  
Radar Data ◽  
Ground Movement ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
The North ◽  
Permanent Scatterers ◽  
Satellite Radar ◽  
Aperture Radar

Land subsidence in Cangzhou of the North China Plain, has been an ongoing problem for the past four decades (since the later 1970s). With the development of new synthetic aperture radar(SAR)sensors and interferometric synthetic aperture radar(InSAR) techniques, the application of satellite Radar data have enhanced capabilities to detect and monitor ground displacements with centimeter to millimeter precision at greater spatial detail and higher temporal resolution. We use Permanent Scatterers interferometric synthetic aperture radar（PS-InSAR）technology (Hooper, A.2004) to detect and measure ground movement in this area（from2004 to 2007）. Results of the cangzhou region study are reported and the utility of the InSAR methodology is discussed.

Assessment of the Seismic Hazards of the Marikina Valley Fault from 2019 Mw 6.1 Castillejos Earthquake and Historical Events

2021 ◽  
Author(s):  
Ying-Hui Yang ◽  
Min-Chien Tsai ◽  
Jyr-Ching Hu ◽  
Qiang Chen ◽  
Mario Aurelio ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Fault System ◽  
Seismogenic Zone ◽  
Coseismic Deformation ◽  
Interferometric Synthetic Aperture Radar ◽  
Sar Images ◽  
Strong Earthquakes ◽  
Coulomb Failure ◽  
Aperture Radar

Abstract The 2019 Mw 6.1 Castillejos earthquake occurred in the Zambales range of the central Luzon Island in Philippines. No active fault was reported around the seismogenic zone according to previous investigations. This earthquake draws attention for assessment in seismic risk along the Marikina Valley fault system (MVFS) near the Manila dense metropolitan population. The Coulomb failure stress (CFS) change on the MVFS is estimated by the coseismic faulting model derived from the inversion of coseismic deformation field observed from the Differential Interferometric Synthetic Aperture Radar using both the Advanced Land Observing Satellite-2 and Sentinel-1 Synthetic Aperture Radar (SAR) images. The predicted CFS change is less than 0.5 kPa that implies insignificant Coulomb stress accumulation on the MVFS after the Mw 6.1 Castillejos event. However, the recorded 14 moderate and strong earthquakes in and around the Luzon islands caused significant CFS drop on the MVFS. This might delay the occurrence of the earthquake for 0.2–50 yr on the MVFS.

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.

Study on CR Point Identification in SAR Images

2012 ◽  
Vol 204-208 ◽  
pp. 1754-1757
Author(s):  
Xue Min Xing
Keyword(s):  
Synthetic Aperture Radar ◽  
Correlation Coefficient ◽  
Traditional Method ◽  
Deformation Monitoring ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Corner Reflector ◽  
Sar Images ◽  
Aperture Radar

In the algorithm of Corner Reflector Interferometric Synthetic Aperture Radar (CR-InSAR), the identification of Corner Reflector (CR) points in SAR images is necessary. Due to the uncertainty of traditional method in estimating the row and column information of CR, this paper presents a method for CR points’ identification, which is based on the intensity and correlation coefficient. The method has been successfully used to find the CR points in the six SAR images of the study area where the identification of CR points installed along a high way is difficult. The results show that the method presented is effective and reliable which will play important role in the deformation monitoring in highway using CR-InSAR algorithm.

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.

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.

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