Simulation assisted high-resolution psi analysis

Since the first demonstration of the potential of the differential SAR interferometry in the early 1990s a lot of effort has been made to accurately estimate ground deformation with imaging radar sensors. This led to the invention of the Persistent Scatterer Interferometry (PSI) in the late 1990s. PSI enables the estimation of ground deformation for a set of temporally stable radar reflectors, the so called PS, with millimeter accuracy. The main advantage compared to methods commonly used for ground deformation monitoring like GPS is the possibility to cover large areas very economically. One of the main drawbacks is the opportunistic sampling of the target area, which is mainly governed by the distribution of stable radar reflectors within the scene. Besides problems caused by undersampling the main issue is due to the face, that the real world feature related to a PS is usually not known. This makes the interpretation of the results particularly difficult. While the assignment of these real world features is very difficult in the case of ERS like sensors, modern high resolution SAR sensors like TerraSAR-X (TSX) render this task possible. We investigate the use of SAR simulation to match real world features with PS extracted from a TSX stack acquired over the city of Berlin Germany. The simulation is based on a 3D city model of the area around the Potsdamer Platz, Berlin.National Natural Science Foundation of China/6095011035

Моделирование радиолокационных изображений с использованием программно-моделирующего конструктора радиолокационных карт

В работе приводится модель получения радиолокационного изображения трёхмерной модели сцены. Сцена представляет собой фацетную модель формируемую с помощью конструктора радиолокационных карт. При моделировании радиолокационного изображения используются модели диффузного и зеркального отражения от элементов сцены. В работе приведен результат моделирования радиолокационного изображения при помощи конструктора радиолокационных карт

Hardware-Accelerated SAR Simulation with NVIDIA-RTX Technology

Synthetic Aperture Radar (SAR) is a critical sensing technology that is notably independent of the sensor-to-target distance and has numerous cross-cutting applications, e.g., target recognition, mapping, surveillance, oceanography, geology, forestry (biomass, deforestation), disaster monitoring (volcano eruptions, oil spills, flooding), and infrastructure tracking (urban growth, structure mapping). SAR uses a high-power antenna to illuminate target locations with electromagnetic radiation, e.g., 10GHz radio waves, and illuminated surface backscatter is sensed by the antenna which is then used to generate images of structures. Real SAR data is difficult and costly to produce and, for research, lacks a reliable source ground truth. This article proposes a open source SAR simulator to compute phase histories for arbitrary 3D scenes using newly available ray-tracing hardware made available commercially through the NVIDIA's RTX graphics cards series. The OptiX GPU ray tracing library for NVIDIA GPUs is used to calculate SAR phase histories at unprecedented computational speeds. The simulation results are validated against existing SAR simulation code for spotlight SAR illumination of point targets. The computational performance of this approach provides orders of magnitude speed increases over CPU simulation. An additional order of magnitude of GPU acceleration when simulations are run on RTX GPUs which include hardware specifically to accelerate OptiX ray tracing. The article describes the OptiX simulator structure, processing framework and calculations that afford execution on massively parallel GPU computation device. The shortcoming of the OptiX library's restriction to single precision float representation is discussed and modifications of sensitive calculations are proposed to reduce truncation error thereby increasing the simulation accuracy under this constraint.Comment: 17 pages, 7 figures, Algorithms for Synthetic Aperture Radar Imagery XXVII, SPIE Defense + Commercial Sensing 202

Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks

This paper addresses the highly challenging problem of automatically detecting man-made structures especially buildings in very high resolution (VHR) synthetic aperture radar (SAR) images. In this context, the paper has two major contributions: Firstly, it presents a novel and generic workflow that initially classifies the spaceborne TomoSAR point clouds $-$ generated by processing VHR SAR image stacks using advanced interferometric techniques known as SAR tomography (TomoSAR) $-$ into buildings and non-buildings with the aid of auxiliary information (i.e., either using openly available 2-D building footprints or adopting an optical image classification scheme) and later back project the extracted building points onto the SAR imaging coordinates to produce automatic large-scale benchmark labelled (buildings/non-buildings) SAR datasets. Secondly, these labelled datasets (i.e., building masks) have been utilized to construct and train the state-of-the-art deep Fully Convolution Neural Networks with an additional Conditional Random Field represented as a Recurrent Neural Network to detect building regions in a single VHR SAR image. Such a cascaded formation has been successfully employed in computer vision and remote sensing fields for optical image classification but, to our knowledge, has not been applied to SAR images. The results of the building detection are illustrated and validated over a TerraSAR-X VHR spotlight SAR image covering approximately 39 km$^2$ $-$ almost the whole city of Berlin $-$ with mean pixel accuracies of around 93.84%Comment: Accepted publication in IEEE TGR

SARCASTIC v2.0 - High-performance SAR simulation for next-generation ATR systems

Synthetic aperture radar has been a mainstay of the remote sensing field for many years, with a wide range of applications across both civilian and military contexts. However, the lack of openly available datasets of comparable size and quality to those available for optical imagery has severely hampered work on open problems such as automatic target recognition, image understanding and inverse modelling. This paper presents a simulation and analysis framework based on the upgraded SARCASTIC v2.0 engine, along with a selection of case studies demonstrating its application to well-known and novel problems. In particular, we demonstrate that SARCASTIC v2.0 is capable of supporting complex phase-dependent processing such as interferometric height extraction whilst maintaining near-realtime performance on complex scenes

Intricate multiple scattering features of artificial facilities in X-Band SAR images

Due to the intricate distortion and reflection geometry of the SAR signal, it is typically difficult to determine the multiple scattering of large artificial objects in SAR images. This work presents a scattering point path tracking model that utilizes the real three-dimensional dimensions of targets, based on the geometric optics method. Three different artificial structures, including light poles, cable stayed bridges, and power transmission lines, are carefully analysed in time-series SAR images with their simulated multiple scattering results. The results demonstrate that the routes determined by the model are consistent with the multiple scattering features on SAR images. Moreover, the time-series data demonstrate that ripples in the water's surface have a significant impact on the multi-scattering features of power lines and bridges. The double scattering features of the light pole provides a novel approach to the process of permanent scatterers (PS) in urban areas. The instances presented in this study demonstrate the effectiveness of the scattering point path tracking model in identifying the various artificial facility targets on different reflective surfaces. It will be a useful tool for deciphering the multiple scattering of large artificial structures when their 3D model is known

Damage Proxy Map from Interferometric Synthetic Aperture Radar Coherence

A method, apparatus, and article of manufacture provide the ability to generate a damage proxy map. A master coherence map and a slave coherence map, for an area prior and subsequent to (including) a damage event are obtained. The slave coherence map is registered to the master coherence map. Pixel values of the slave coherence map are modified using histogram matching to provide a first histogram of the master coherence map that exactly matches a second histogram of the slave coherence map. A coherence difference between the slave coherence map and the master coherence map is computed to produce a damage proxy map. The damage proxy map is displayed with the coherence difference displayed in a visually distinguishable manner

Using Ray Tracing to Improve Bridge Monitoring With High-Resolution SAR Satellite Imagery

While satellite Persistent Scatterer SAR Interferometry (PSI) is an effective technique to monitor the health of structures via selection of long-term coherent pixels, detailed interpretation of displacement measurements requires knowledge of which surfaces, the reflection is coming from. Ray tracing algorithms can be used to simulate SAR backscatter for structures, and link observed PS pixels to specific parts of structures. We investigate the reflectivity of three bridges in London for a high-resolution TerraSAR-X dataset, using a ray tracing technique. Artificial reflectors are mounted on one of the bridges. We compare the simulated backscatter with the location of points selected as PS pixels using a stack of 38 TerraSAR-X images. The results confirm that we can predict overall scattering behaviour of a bridge using SAR simulation techniques when we have access to a 3D model of the structure. However, the results of simulation depend on the level of details in the 3D model and a high-detailed 3D model including corner reflectors allows the ray tracing technique to perfectly simulate position of the strong scatterers. This approach can help designers increase the SAR reflectivity of a bridge in the design phase of structural bridge assets, or in a retrofit phase, by installing artificial reflectors. We also link the strong scatterers in the reflectivity map to the corresponding scattering surfaces in the structural model that contributed to the signal. This allows the end-users of the InSAR products to better understand which sections of a bridge are moving when a PS pixel indicates displacement