scholarly journals An Anisotropic Scattering Analysis Method Based on the Statistical Properties of Multi-Angular SAR Images

2020 ◽  
Vol 12 (13) ◽  
pp. 2152
Author(s):  
Fei Teng ◽  
Yun Lin ◽  
Yanping Wang ◽  
Wenjie Shen ◽  
Shanshan Feng ◽  
...  
Keyword(s):  
Rayleigh Distribution ◽  
Anisotropic Scattering ◽  
Statistical Properties ◽  
Synthetic Aperture ◽  
Ratio Test ◽  
Analysis Method ◽  
Sar Images ◽  
X Band ◽  
Sar Data ◽  
Single Polarization

The scatterings of many targets are aspect dependent, which is called anisotropy. Multi-angular synthetic aperture radar (SAR) is a suitable means of detecting this kind of anisotropic scattering behavior by viewing targets from different aspect angles. First, the statistical properties of anisotropic and isotropic scatterings are studied in this paper. X-band chamber circular SAR data are used. The result shows that isotropic scatterings have stable distributions in different aspect viewing angles while the distributions of anisotropic scatterings are various. Then the statistical properties of single polarization high-resolution multi-angular SAR images are modeled by different distributions. G 0 distribution performs best in all types of areas. An anisotropic scattering analysis method based on the multi-angular statistical properties is proposed. A likelihood ratio test based on G 0 distribution is used to measure the anisotropy. Anisotropic scatterings can be discriminated from isotropic scatterings by thresholding. Besides, the scattering direction can also be estimated by our method. AHH polarization C-band circular SAR data are used to validate our method. The result of using G 0 distribution is compared with the result of using Rayleigh distribution. The result of using G 0 distribution is the better one.

scholarly journals Brief Communication: Rapid mapping of landslide events: the 3 December 2013 Montescaglioso landslide, Italy

2014 ◽  
Vol 14 (7) ◽  
pp. 1835-1841 ◽  
Author(s):  
A. Manconi ◽  
F. Casu ◽  
F. Ardizzone ◽  
M. Bonano ◽  
M. Cardinali ◽  
...  
Keyword(s):  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Southern Italy ◽  
Surface Deformation ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Main Road ◽  
Rapid Mapping ◽  
X Band ◽  
Sar Data

Abstract. We present an approach to measure 3-D surface deformations caused by large, rapid-moving landslides using the amplitude information of high-resolution, X-band synthetic aperture radar (SAR) images. We exploit SAR data captured by the COSMO-SkyMed satellites to measure the deformation produced by the 3 December 2013 Montescaglioso landslide, southern Italy. The deformation produced by the deep-seated landslide exceeded 10 m and caused the disruption of a main road, a few homes and commercial buildings. The results open up the possibility of obtaining 3-D surface deformation maps shortly after the occurrence of large, rapid-moving landslides using high-resolution SAR data.

scholarly journals Aspect Entropy Extraction Using Circular SAR Data and Scattering Anisotropy Analysis

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 346 ◽  
Author(s):  
Fei Teng ◽  
Wen Hong ◽  
Yun Lin
Keyword(s):  
Anisotropic Scattering ◽  
Synthetic Aperture ◽  
Viewing Angle ◽  
Denoising Method ◽  
Discrimination Capability ◽  
X Band ◽  
Different Types ◽  
Degree Of Anisotropy ◽  
Sar Data ◽  
Scattering Anisotropy

In conventional synthetic aperture radar (SAR) working modes, targets are assumed isotropic because the viewing angle is small. However, most man-made targets are anisotropic. Therefore, anisotropy should be considered when the viewing angle is large. From another perspective, anisotropy is also a useful feature. Circular SAR (CSAR) can detect the scattering variation under different azimuthal look angles by a 360-degree observation. Different targets usually have varying degrees of anisotropy, which aids in target discrimination. However, there is no effective method to quantify the degree of anisotropy. In this paper, aspect entropy is presented as a descriptor of the scattering anisotropy. The range of aspect entropy is from 0 to 1, which corresponds to anisotropic to isotropic. First, the method proposed extracts aspect entropy at the pixel level. Since the aspect entropy of pixels can discriminate isotropic and anisotropic scattering, the method prescreens the target from the isotropic clutters. Next, the method extracts aspect entropy at the target level. The aspect entropy of targets can discriminate between different types of targets. Then, the effect of noise on aspect entropy extraction is analyzed and a denoising method is proposed. The Gotcha public release dataset, an X-band circular SAR data, is used to validate the method and the discrimination capability of aspect entropy.

scholarly journals Characterizing the Statistical Properties of SAR Clutter by Using an Empirical Distribution

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Gui Gao ◽  
Gongtao Shi ◽  
Huanxin Zou ◽  
Shilin Zhou
Keyword(s):  
Synthetic Aperture Radar ◽  
Empirical Model ◽  
Empirical Distribution ◽  
Statistical Properties ◽  
Synthetic Aperture ◽  
Statistical Characteristics ◽  
Sar Images ◽  
Proposed Model ◽  
Wide Range ◽  
Sar Data

The performances on the applications of synthetic aperture radar (SAR) data strongly depend on the statistical characteristics of the pixel amplitudes or intensities. In this paper, a new empirical model, called simplyℋ𝒢o, has been proposed to characterize the statistical properties of SAR clutter data over the wide range of homogeneous, heterogeneous, and extremely heterogeneous returns of terrain classes. A particular case of theℋ𝒢odistribution is the well-known𝒢odistributions. We also derived analytically the estimators of the presentedℋ𝒢omodel by applying the “method of log cumulants” (MoLCs). The performance of the proposed model is verified by using some measured SAR images.

scholarly journals Water Extraction from Fully Polarized SAR Based on Combined Polarization and Texture Features

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3332
Author(s):  
Jikang Wan ◽  
Jiayi Wang ◽  
Min Zhu
Keyword(s):  
Coastal Wetland ◽  
Single Channel ◽  
Texture Features ◽  
Water Bodies ◽  
Polarization Method ◽  
Sar Images ◽  
Research Areas ◽  
Sar Data ◽  
Single Polarization ◽  
Occurrence Matrix

Given the limited features (for example, the backscattering coefficient threshold range) of single-channel Synthetic Aperture Radar (SAR) images, it is difficult to distinguish ground objects similar to the backscattering coefficients of water bodies. In this paper, two representative research areas are selected (Yancheng Coastal wetland and Shijiu Lake), and the fully polarized SAR data based on Gaofen-3 are used to extract water bodies using the method of polarization decomposition and gray level co-occurrence matrix. Firstly, the multi-dimensional features of ground objects are extracted, and then the redundancy processing of multi-dimensional features is carried out by the separability index, which effectively solves the misclassification of non-water bodies and water bodies and improves the accuracy of water body extraction. The comparison between the results of full-polarization extraction and single-polarization extraction shows that both full-polarization and single-polarization extraction can extract water information, but the extraction accuracy of the full-polarization method can reach 94.74% in the area with complex wetland features, which can effectively compensate for the lack of precision of the single-polarization method. Although multi-dimensional features can be extracted from fully polarimetric SAR data, data redundancy may exist. Therefore, using the Separability index (SI) to process multi-dimensional features can effectively solve the problem of feature redundancy and improve classification accuracy.

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.

Extreme Waves Detected by Satellite Borne Synthetic Aperture Radar

2002 ◽  
Author(s):  
Susanne Lehner ◽  
Johannes Schulz-Stellenfleth ◽  
Andreas Niedermeier ◽  
Jochen Horstmann ◽  
Wolfgang Rosenthal
Keyword(s):  
Synthetic Aperture Radar ◽  
Wave Height ◽  
Sampling Rate ◽  
Ocean Wave ◽  
Synthetic Aperture ◽  
Extreme Waves ◽  
Sar Images ◽  
Data Set ◽  
Sar Data ◽  
Aperture Radar

Within the last 20 years at least 200 supercarriers have been lost, due to severe weather conditions. In many cases the cause of accidents is believed to be ‘rouge waves’, which are individual waves of exceptional wave height or abnormal shape. I situ measurements of extreme waves are scarce and most observations are reported by ship masters after the encounter. In this paper a global set of synthetic aperture radar (SAR) images is used to detect extreme ocean wave events. The data were acquired aboard the European remote sensing satellite ERS-2 every 200 km along the track. As the data are not available as a standard product of the Europea Space Agency (ESA), the radar raw data were focused to complex SAR images using the processor BSAR developed by the German Aerospace Center. The entire SAR data set covers 27 days representing 34000 SAR imagettes with a size of 5km×10km. Complex SAR data contain information on ocean wave height, propagation direction and grouping as well as on ocean surface winds. Combining all of this information allows to extract and locate extreme waves from complex SAR images on a global basis. Special algorithms have been developed to retrieve the following parameters from the SAR data: Wind speed and direction, significant wave height, wave direction, wave groups and their individual heights. The satellite ENVISAT launched in March 2002 acquires SAR data with an even higher sampling rate (every 100 km). It is expected that a long-term analysis of ERS and ENVISAT data will give new insight into the physical processes responsible for rogue wave generation. Furthermore, the identification of hot spots will contribute to the optimization of ship routes.

scholarly journals COMPARISON OF C-BAND AND X-BAND POLARIMETRIC SAR DATA FOR RIVER ICE CLASSIFICATION ON THE PEACE RIVER

2016 ◽  
Vol XLI-B7 ◽  
pp. 543-548
Author(s):  
H. Łoś ◽  
K. Osińska-Skotak ◽  
J. Pluto-Kossakowska ◽  
M. Bernier ◽  
Y. Gauthier ◽  
...  
Keyword(s):  
Supervised Classification ◽  
Open Water ◽  
Covariance Matrices ◽  
Synthetic Aperture ◽  
River Ice ◽  
Data Sets ◽  
Satellite Systems ◽  
Peace River ◽  
X Band ◽  
Sar Data

In this study, synthetic aperture radar (SAR) data from TerraSAR-X were compared with RADARSAT-2 data to evaluate their effectiveness for river ice monitoring on the Peace River. For several years RADARSAT-2 data have been successfully used for river ice observation. However, it is important to take into account data from other satellites as they may provide solutions when it is not possible to obtain images from the preferred system (e.g., in the case of acquisition priority conflicts). In this study we compared three TerraSAR-X (X-band) and three RADARSAT-2 (C-band) datasets acquired in December 2013 on a section of the Peace River, Canada. For selected classes (open water, skim ice, juxtaposed skim ice, agglomerated skim ice, frazil run and consolidated ice) we compared backscattering values in HH and VV polarisation and performed Wishart supervised classification. Covariance matrices that were previously filtered using a refined Lee filter were used as input data for classification. For all data sets the overall accuracy was higher than 80%. Similar errors associated with classification output were observed for data from both satellite systems.

scholarly journals A multiscale approach for detection and mapping differential subsidence using multi-platform InSAR products

2020 ◽  
Vol 382 ◽  
pp. 173-177
Author(s):  
Dario E. Solano-Rojas ◽  
Shimon Wdowinski ◽  
Enrique Cabral-Cano ◽  
Batuhan Osmanoglu ◽  
Emre Havazli ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Data Availability ◽  
Synthetic Aperture ◽  
Multiscale Approach ◽  
Interferometric Synthetic Aperture Radar ◽  
Case Examples ◽  
X Band ◽  
Data Coverage ◽  
Sar Data ◽  
Aperture Radar

Abstract. Detecting and mapping subsidence is currently supported by interferometric synthetic aperture radar (InSAR) products. However, several factors, such as band-dependent processing, noise presence, and strong subsidence limit the use of InSAR for assessing differential subsidence, which can lead to ground instability and damage to infrastructure. In this work, we propose an approach for measuring and mapping differential subsidence using InSAR products. We consider synthetic aperture radar (SAR) data availability, data coverage over time and space, and the region's subsidence rates to evaluate the need of post-processing, and only then we interpret the results. We illustrate our approach with two case-examples in Central Mexico, where we process SAR data from the Japanese ALOS (L-band), the German TerraSAR-X (X-band), the Italian COSMO-SkyMed (X-band) and the European Sentinel-1 (C-band) satellites. We find good agreement between our results on differential subsidence and field data of existing faulting and find potential to map yet-to-develop faults.

scholarly journals Integration of Automatic Identification System (AIS) Data and Single-Channel Synthetic Aperture Radar (SAR) Images by SAR-Based Ship Velocity Estimation for Maritime Situational Awareness

2019 ◽  
Vol 11 (19) ◽  
pp. 2196 ◽  
Author(s):  
Maria Daniela Graziano ◽  
Alfredo Renga ◽  
Antonio Moccia
Keyword(s):  
Synthetic Aperture Radar ◽  
Velocity Estimation ◽  
Synthetic Aperture ◽  
Automatic Identification ◽  
Identification System ◽  
Sar Images ◽  
Automatic Identification System ◽  
Route Information ◽  
Sar Data ◽  
Aperture Radar

The synergic utilization of data from different sources, either ground-based or spaceborne, can lead to effective monitoring of maritime activities. To this end, the integration of synthetic aperture radar (SAR) images with data reported by the automatic identification system (AIS) is of high interest. Accurate matching of ships detected in SAR images with AIS data requires compensation of the azimuth offset, which depends on the ship’s velocity. The existing procedures interpolate the route information gathered by AIS to estimate the ship’s velocity at the epoch of the SAR data, to remove the offset. Matching accuracy is limited by interpolation errors and AIS route information unavailability or uncertainties. This paper proposes the use of SAR-based ship velocity estimations to improve the integration of AIS and SAR data. A case study has been analyzed, in which the method has been tested on TerraSAR-X images collected over the Gulf of Naples, Italy. Presented results show that the matching is improved with respect to standard procedures. The proposed method limits the distance between the AIS report and the SAR-based detection to less than 150 m, which is in line with maritime surveillance needs.

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