A Research on 3-D point clouds reconstruction of SAR images driven by electromagnetic scattering mechanisms

2021 ◽  
Author(s):  
Zhilong Yang ◽  
Feng Wang ◽  
Feng Xu
Keyword(s):  
Electromagnetic Scattering ◽  
Point Clouds ◽  
Sar Images ◽  
Scattering Mechanisms

scholarly journals Research on a Method for Simulating Multiview Ocean Wave Synchronization Data by Networked SAR Satellites

2019 ◽  
Vol 7 (6) ◽  
pp. 180 ◽  
Author(s):  
Yong Wan ◽  
Xiaoyu Zhang ◽  
Yongshou Dai ◽  
Xiaolei Shi
Keyword(s):  
Electromagnetic Scattering ◽  
Ocean Wave ◽  
Cutoff Wavelength ◽  
Sar Images ◽  
Wind Speeds ◽  
Wave Data ◽  
The Monte Carlo Method ◽  
Data Source ◽  
The Time Domain ◽  
Sea Area

It is expected that the problem of the azimuth cutoff wavelength in single-satellite synthetic aperture radar (SAR) observations can be solved by means of the cooperative observation of networked SAR satellites. Multiview SAR wave synchronization data are required in the process. However, most of the current orbiting satellites are geosynchronous orbit satellites; the simultaneous observation by multiple SARs in the same sea area cannot be achieved, and multiview synchronization data cannot be obtained. Therefore, this paper studies the simulation of the multiview SAR wave synchronization data. Ocean wave spectra were simulated by using the Pierson Moskowitz (PM) spectrum. The Monte Carlo method was used to simulate two-dimensional (2D) ocean surfaces at different wind speeds. The two-scale electromagnetic scattering model was used to calculate the ocean surface backscattering coefficient, and the time-domain echo algorithm was used to generate echo signals. The echo signals were processed by the Range–Doppler (RD) imaging algorithm to obtain ocean SAR data. Based on the obtained single-SAR wave data, networked satellites consisting of three SARs were simulated, and the SAR wave data were synchronized. The results show that when SARs are used to observe the same sea area from different observation directions, the clarity of the wave fringes in the SAR images are different. For different azimuth angles, the degrees of azimuth cutoff are different. These results reflect the influences of different degrees of azimuth cutoff on SAR images. The simulated wave synchronization data can be used as the basic data source for subsequent azimuth cutoff wavelength compensation.

Combining spaceborne SAR images with 3D point clouds for infrastructure monitoring applications

2016 ◽  
Vol 111 ◽  
pp. 45-61 ◽  
Author(s):  
Andrei Anghel ◽  
Gabriel Vasile ◽  
Rémy Boudon ◽  
Guy d’Urso ◽  
Alexandre Girard ◽  
...  
Keyword(s):  
Point Clouds ◽  
Sar Images ◽  
Infrastructure Monitoring ◽  
3D Point Clouds ◽  
Monitoring Applications

scholarly journals Fractal-Based Local Range Slope Estimation from Single SAR Image with Applications to SAR Despeckling and Topographic Mapping

2018 ◽  
Vol 10 (8) ◽  
pp. 1294 ◽  
Author(s):  
Gerardo Di Martino ◽  
Alessio Di Simone ◽  
Daniele Riccio
Keyword(s):  
Electromagnetic Scattering ◽  
Incidence Angle ◽  
Shape From Shading ◽  
Surface Shape ◽  
Retrieval Algorithm ◽  
Surface Model ◽  
Topographic Mapping ◽  
Sar Images ◽  
Direct Model ◽  
Slope Estimation

In this paper, we propose a range slope estimation procedure from single synthetic aperture radar (SAR) images with both methodological and applicative innovations. The retrieval algorithm is based on an analytical linearized direct model, which relates the SAR intensity data to the range local slopes and encompasses both a surface model and an electromagnetic scattering model. Scene topography is described via fractal geometry, whereas the Small Perturbation Method is adopted to represent the scattering behavior of the surface. The range slope map is then used to estimate the surface topography and the local incidence angle map. For topographic mapping applications, also referred to as shape from shading, a regularization procedure is derived to recover the azimuth local slope and reduce distortions. Then we present a new intriguing application of the inversion procedure in the field of SAR despeckling. Proposed techniques and high-level products are tested in a wide series of experiments, where the algorithms are applied to both simulated (canonical) and actual SAR images. It is proved that the proposed range slope retrieval technique can (1) provide an estimate of the surface shape, with overall better performance w.r.t. typical models used in this field and (2) be useful in advanced despeckling techniques.

scholarly journals Geocoding Error Correction for InSAR Point Clouds

2018 ◽  
Vol 10 (10) ◽  
pp. 1523 ◽  
Author(s):  
Sina Montazeri ◽  
Fernando Rodríguez González ◽  
Xiao Zhu
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Ground Control ◽  
Control Points ◽  
Persistent Scatterer Interferometry ◽  
Sar Images ◽  
Localization Accuracy ◽  
Ground Control Points ◽  
Persistent Scatterer ◽  
The City

Persistent Scatterer Interferometry (PSI) is an advanced multitemporal InSAR technique that is capable of retrieving the 3D coordinates and the underlying deformation of time-coherent scatterers. Various factors degrade the localization accuracy of PSI point clouds in the geocoding process, which causes problems for interpretation of deformation results and also making it difficult for the point clouds to be compared with or integrated into data from other sensors. In this study, we employ the SAR imaging geodesy method to perform geodetic corrections on SAR timing observations and thus improve the positioning accuracy in the horizontal components. We further utilize geodetic stereo SAR to extract large number of highly precise ground control points (GCP) from SAR images, in order to compensate for the unknown height offset of the PSI point cloud. We demonstrate the applicability of the approach using TerraSAR-X high resolution spotlight images over the city of Berlin, Germany. The corrected results are compared with a reference LiDAR point cloud of Berlin, which confirms the improvement in the geocoding accuracy.

scholarly journals Scattering Characteristics of Targets above a Rough Surface in SAR Images

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Bo Zhao ◽  
Tie Jun Cui
Keyword(s):  
Rough Surface ◽  
Electromagnetic Scattering ◽  
Fourier Transforms ◽  
Coupling Effect ◽  
Frequency Method ◽  
Sar Images ◽  
X Band ◽  
Scattering Centers ◽  
Scattered Fields ◽  
Target Characteristics

The modeling, simulation, and analysis of target characteristics are essential to a synthetic aperture radar (SAR) image-based autotarget recognition (ATR) system. The coupling effect between targets and rough surface is also important to the electromagnetic scattering and remote sensing. In this work, the simulations to SAR images of targets above a finite rough surface have been investigated. The effect of rough surface on the target characteristics, or the coupling between the rough surface and targets, is analyzed in details by observing changes of locations and intensities of scattering centers in the SAR images. The SAR images are obtained by taking two-dimensional inverse fast Fourier transforms (FFTs) of the scattered fields, which are computed by the combined high-frequency method of shooting and bouncing ray (SBR) and truncated-wedge incremental-length diffraction coefficients (TW-ILDCs). Simulated results of SAR images for complicated targets above a rough surface are given under the 0.25 × 0.25 m2resolution at the X band, in which the coupling effect between targets and rough surface has been studied in details.

scholarly journals A Convolutional Neural Network Combined with Attributed Scattering Centers for SAR ATR

2021 ◽  
Vol 13 (24) ◽  
pp. 5121
Author(s):  
Yu Zhou ◽  
Yi Li ◽  
Weitong Xie ◽  
Lu Li
Keyword(s):  
Electromagnetic Scattering ◽  
Automatic Target Recognition ◽  
Image Features ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Sar Images ◽  
Stationary Target ◽  
Local Structures ◽  
Scattering Centers ◽  
High Level

It is very common to apply convolutional neural networks (CNNs) to synthetic aperture radar (SAR) automatic target recognition (ATR). However, most of the SAR ATR methods using CNN mainly use the image features of SAR images and make little use of the unique electromagnetic scattering characteristics of SAR images. For SAR images, attributed scattering centers (ASCs) reflect the electromagnetic scattering characteristics and the local structures of the target, which are useful for SAR ATR. Therefore, we propose a network to comprehensively use the image features and the features related to ASCs for improving the performance of SAR ATR. There are two branches in the proposed network, one extracts the more discriminative image features from the input SAR image; the other extracts physically meaningful features from the ASC schematic map that reflects the local structure of the target corresponding to each ASC. Finally, the high-level features obtained by the two branches are fused to recognize the target. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove the capability of the SAR ATR method proposed in this letter.

scholarly journals AIRCRAFT SEGMENTATION IN SAR IMAGES BASED ON IMPROVED ACTIVE SHAPE MODEL

2018 ◽  
Vol XLII-3 ◽  
pp. 2331-2335
Author(s):  
X. Zhang ◽  
B. Xiong ◽  
G. Kuang
Keyword(s):  
Electromagnetic Scattering ◽  
Complex Structure ◽  
Image Interpretation ◽  
Active Shape Model ◽  
Geometric Information ◽  
Sar Images ◽  
Shape Information ◽  
Shape Model ◽  
Active Shape ◽  
Linear Dimensionality Reduction

In SAR image interpretation, aircrafts are the important targets arousing much attention. However, it is far from easy to segment an aircraft from the background completely and precisely in SAR images. Because of the complex structure, different kinds of electromagnetic scattering take place on the aircraft surfaces. As a result, aircraft targets usually appear to be inhomogeneous and disconnected. It is a good idea to extract an aircraft target by the active shape model (ASM), since combination of the geometric information controls variations of the shape during the contour evolution. However, linear dimensionality reduction, used in classic ACM, makes the model rigid. It brings much trouble to segment different types of aircrafts. Aiming at this problem, an improved ACM based on ISOMAP is proposed in this paper. ISOMAP algorithm is used to extract the shape information of the training set and make the model flexible enough to deal with different aircrafts. The experiments based on real SAR data shows that the proposed method achieves obvious improvement in accuracy.

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