Designing repeat ground track (RGT) synthetic aperture radar (SAR) constellations for achieving rapid revisits over key areas is essential to employ spaceborne differential interferometric synthetic aperture radar (D-InSAR) technology in Earth observation missions such as geological disaster monitoring and prediction. In this paper, the features of average revisit time (ART) maps are first introduced and investigated, and then an efficient and resource-friendly approach to calculate the ART of constellations is proposed. On this basis, a systematic method for designing an RGT constellation is provided, incorporating lookup-table-based optimization. Once the requirements of the expected RGT constellation, the incident angle of sensors on the constellation, and the orbital elements of the seed satellite in the constellation are given, the range of the optimal inclination and longitude of the ascending node (LAN) of the seed satellite can be found and then the entire constellation is determined. The proposed method enhances the efficiency of revisit time analysis and avoids the repeated modeling when the observation requirements change. Therefore, it is applicable not only prior to launch but also guides orbital maneuvering to adjust constellation configuration for an effective response to sudden disasters, etc. Finally, multiple RGT constellation design tasks are presented to demonstrate the proposed method.
The microstructure and dynamic behaviors of wax crystals in waxy crude oil are the fundamental reasons for a series of physical phenomena in the process of its transportation.
Since geosynchronous synthetic aperture radar (GEO SAR) has curved trajectories, back-projection algorithm (BPA) greatly fits for its imaging. However, for a scene with height variation, the reference range based on the fixed-height imaging grid under curved trajectories is inaccurate in azimuth back-projection. Resultantly, the GEO SAR image quality will be obviously deteriorated in high resolution imaging. To address the issue, this paper proposed the digital elevation model (DEM)-assisted BPA to realize the accurate high resolution GEO SAR imaging for the scene with height variation. DEM information is utilized to construct the imaging grid in the new method for generating the accurate reference range. Simulation results validate that the proposed method achieves good imaging performance for the scene with height variation.
Current sewage pipeline inspection researches focus on locating defects, with only a few studies addressing the crack fine-grained classification task. However, in engineering applications, such as Pipeline Assessment Certification Program (PACP), subcategory coding of cacks is required. Therefore, this paper studies the fine-grained classification of pipeline cracks. Algorithms based on deep learning have been widely used in the field of sewage pipeline inspection, but these models have a large number of parameters and are computationally time-consuming. Edge or embedded devices with weak GPUs cannot accept the time and storage costs of these large models. To solve the above problems, this paper introduces knowledge distillation (KD) which is one of the mainstream model compression methods. We select the large-scale network Resnet50 as the teacher model and design a lightweight network Combined MobileNetV2 & Inception (CMI) for crack detection as the student model. High-temperature distillation allows the student model to obtain dark knowledge from the pre-trained, well-performing teacher model, which is combined with ground truth (GT) for training. Experiments show that KD is effective. By adjusting the KD hyperparameters, the classification accuracy of the student model is improved by 1.41%, and the F1-score is improved by 1.12%. This demonstrates the robustness of knowledge distillation to improve the performance of small models, laying the foundation for future deployment on multi-sensor automatic detection robots.
The sub-satellite track of geosynchronous synthetic aperture radar (GEO SAR) presents the figure "8" or "O", which causes the great changes of platform motion direction and the different projection of anisotropic irregularities along the line-of-sight (LOS) direction. Due to the almost equal angle velocity to that of Earth, the GEO SAR has smaller ionospheric penetration point (IPP) scanning velocity which is much smaller to the counterpart of the low earth orbit SAR (LEO SAR) while is comparable to the drifting velocity of irregularities, which will affect the effective azimuthal velocity. These facts lead to the consequence that the satellite signals from the GEO SAR would become more vulnerable when they are transmitted in the environment where the ionospheric scintillation occurs. However, few works are focused on these mentioned issues towards the GEO SAR system. In this paper, the impacts of ionospheric scintillation on GEO SAR imaging will be analyzed considering the anisotropy and drifting velocity of irregularities. The anisotropy and drifting velocity effects can both originate from the effect on power spectral density (PSD) of phase screen which is used to model the ionospheric scintillation effects. Based on the data from international geomagnetic reference field (IGRF) and satellite tool kit (STK), the GEO SAR imaging simulations for different GEO SAR orbital configurations and positions are carried out. The simulation results demonstrate that the anisotropy and the drifting velocity of irregularities will cause the changes of stripe direction and affect the quality of GEO SAR images.
Geosynchronous orbit synthetic aperture radar (GEO SAR) has a long integration time and a large imaging scene. Therefore, various nonideal factors are easily accumulated, introducing phase errors and degrading the imaging quality. Within the long integration time, tropospheric status changes with time and space, which will result in image shifts and defocusing. According to the characteristics of GEO SAR, the modeling, and quantitative analysis of background troposphere and turbulence are conducted. For background troposphere, the accurate GEO SAR signal spectrum, which takes into account the time-varying troposphere, is deduced. The influences of different rates of changing (ROC) of troposphere with time are analyzed. Finally, results are verified using the refractive index profile data from Fengyun (FY) 3C satellite and the tropospheric zenith delays data from international GNSS service (IGS). The time–space changes of troposphere can cause image shifts which only depend on the satellite beam-foot velocity and the linear ROC of troposphere. The image defocusing is related to the wavelength, resolution requirement, and the second and higher orders of ROC. The short-wavelength GEO SAR systems are more susceptible to impacts, while L-band GEO SAR will be affected when the integration time becomes longer. Tropospheric turbulence will cause the amplitude and phase random fluctuations resulting in image defocusing. However, in the natural environment, radio waves are very weakly affected by turbulence, and the medium-inclined GEO SAR of L- to C-band will not be affected, while the X-band will be influenced slightly.
Based on its ability to obtain two-dimensional (2D) high-resolution images in all-time and all-weather conditions, spaceborne synthetic aperture radar (SAR) has become an important remote sensing technique and the study of such systems has entered a period of vigorous development. Advanced imaging modes such as radar interferometry, tomography, and multi-static imaging, have been demonstrated. However, current in-orbit spaceborne SARs, which all operate in low Earth orbits, have relatively long revisit times ranging from several days to dozens of days, restricting their temporal sampling rate. Geosynchronous SAR (GEO SAR) is an active research area because it provides significant new capability, especially its much-improved temporal sampling. This paper reviews the research progress of GEO SAR technologies in detail. Two typical orbit schemes are presented, followed by the corresponding key issues, including system design, echo focusing, main disturbance factors, repeat-track interferometry, etc, inherent to these schemes. Both analysis and solution research of the above key issues are described. GEO SAR concepts involving multiple platforms are described, including the GEO SAR constellation, GEO-LEO/airborne/unmanned aerial vehicle bistatic SAR, and formation flying GEO SAR (FF-GEO SAR). Due to the high potential of FF-GEO SAR for three-dimensional (3D) deformation retrieval and coherence-based SAR tomography (TomoSAR), we have recently carried out some research related to FF-GEO SAR. This research, which is also discussed in this paper, includes developing a formation design method and an improved TomoSAR processing algorithm. It is found that GEO SAR will continue to be an active topic in the aspect of data processing and multi-platform concept in the near future.
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