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

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.

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

Natural Hazards and Earth System Science ◽

10.5194/nhess-14-1835-2014 ◽

2014 ◽

Vol 14 (7) ◽

pp. 1835-1841 ◽

Cited By ~ 43

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.

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

Remote Sensing ◽

10.3390/rs13245091 ◽

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.

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Extreme Waves Detected by Satellite Borne Synthetic Aperture Radar

21st International Conference on Offshore Mechanics and Arctic Engineering, Volume 2 ◽

10.1115/omae2002-28293 ◽

2002 ◽

Cited By ~ 1

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.

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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

Remote Sensing ◽

10.3390/rs11192196 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2196 ◽

Cited By ~ 6

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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High-Resolution SAR Image Despeckling Based on Nonlocal Means Filter and Modified AA Model

Security and Communication Networks ◽

10.1155/2020/8889317 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Qiao Ke ◽

Sun Zeng-guo ◽

Yang Liu ◽

Wei Wei ◽

Marcin Woźniak ◽

...

Keyword(s):

High Resolution ◽

Synthetic Aperture Radar ◽

Iterative Algorithm ◽

Synthetic Aperture ◽

Sar Image ◽

Sar Images ◽

Edge Information ◽

Nonlocal Means ◽

Proposed Model ◽

Effective Performance

A new speckle suppression algorithm is proposed for high-resolution synthetic aperture radar (SAR) images. It is based on the nonlocal means (NLM) filter and the modified Aubert and Aujol (AA) model. This method takes the nonlocal Dirichlet function as a linear regularization item, which constructs the weight by measuring the similarity of images. Then, a new despeckling model is introduced by combining the regularization item and the data item of the AA model, and an iterative algorithm is proposed to solve the new model. The experiments show that, compared with the AA model, the proposed model has more effective performance in suppressing speckle; namely, ENL and DCV measures are 21.75% and 4.5% higher, respectively, than for NLM. Moreover, it also has better performance in keeping the edge information.

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

Remote Sensing ◽

10.3390/rs12132152 ◽

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.

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Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽

10.3390/s21051643 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1643

Author(s):

Ming Liu ◽

Shichao Chen ◽

Fugang Lu ◽

Mengdao Xing ◽

Jingbiao Wei

Keyword(s):

Synthetic Aperture Radar ◽

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Synthetic Aperture ◽

False Alarms ◽

Sar Image ◽

Constant False Alarm Rate ◽

Sar Images ◽

Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

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