Single-Domain Generalized Object Detection (S-DGOD) aims to train on a single source domain for robust performance across a variety of unseen target domains by taking advantage of an object detector. Existing S-DGOD approaches often rely on data augmentation strategies, including a composition of visual transformations, to enhance the detector's generalization ability. However, the absence of real-world prior knowledge hinders data augmentation from contributing to the diversity of training data distributions. To address this issue, we propose PhysAug, a novel physical model-based non-ideal imaging condition data augmentation method, to enhance the adaptability of the S-DGOD tasks. Drawing upon the principles of atmospheric optics, we develop a universal perturbation model that serves as the foundation for our proposed PhysAug. Given that visual perturbations typically arise from the interaction of light with atmospheric particles, the image frequency spectrum is harnessed to simulate real-world variations during training. This approach fosters the detector to learn domain-invariant representations, thereby enhancing its ability to generalize across various settings. Without altering the network architecture or loss function, our approach significantly outperforms the state-of-the-art across various S-DGOD datasets. In particular, it achieves a substantial improvement of 7.3% and 7.2% over the baseline on DWD and Cityscape-C, highlighting its enhanced generalizability in real-world settings.

Ground Penetrating Radar (GPR) is a powerful tool for exploring the shallow subsurface due to its effective and noninvasive features. Recently, accurate and high-resolution characterization of subsurface properties in three-dimensional (3D) GPR investigations calls for a quantitative and high-resolution imaging approach. However, the full-waveform inversion (FWI) method for GPR data was performed mostly in 2D and rarely discussed the polarizations. To fully utilize 3D GPR polarization data, this letter proposes a frequency-domain FWI algorithm for simultaneous inversion of both the co-polarized and cross-polarized data. Detail derivations and vital processes in our inversion workflow were described in detail, before applying it to the numerical experiments and analyzing the potential impacts of the polarizations on inversion results with a synthetic model. Results showed that the cross-polarized data is more sensitive than the co-polarized data in inversion, and the behaviors in the inversion of the multi-polarized data with different values in the weighting matrix suggests that larger weights for co-polarized data is of benefit to a better inversion result.

Theoretical advancements in full waveform inversion (FWI) of ground-penetrating radar (GPR) data have shown promising potential for enhancing the accuracy of GPR data interpretation. However, the widespread implementation of FWI faces significant challenges due to its low-computational efficiency and high memory consumption, primarily attributed to the gradient operation stage. To address these issues, we propose a spatiotemporal optimization approach for GPR FWI based on super-resolution (SR) technology. The proposed method focuses on three optimization directions: adopting a storage strategy that only preserves the forward wavefield while synchronizing the gradient operation and adjoint wavefield operation, compressing the time dimension of the GPR wavefield based on the Nyquist sampling law, and obtaining a fuzzy gradient in the spatial dimension by sampling the wavefield at each moment and restoring it using an SR network to complete the FWI. Experimental results demonstrate that the proposed optimization method achieves a nearly 50% acceleration in computational efficiency without compromising the original inversion architecture. Moreover, it reduces the memory usage to approximately 4.17% of the original memory, while maintaining the effectiveness of the inversion process. This method exhibits practicality and effectiveness through several numerical and measured data experiments, providing a solid foundation for the widespread application of FWI on commonly available microcomputers.

Reverse time migration (RTM) is an important technology for imaging ground penetrating radar (GPR) data. To address the problem of artifacts flooding of imaging results and high memory consumption of RTM, we propose an optimized multitraces cross correlation window (MCW) to increase the order of magnitude difference between the signals and artifacts for more obvious separation effect, but it also exacerbates the problem of computational cost. With the high sampling rate and high efficiency of collection method, common offset GPR is convenient to acquire large amounts of data, which consumes more numerous cost for RTM. Due to the attenuation property of high-frequency radar waves, most of the signals of common offset GPR originate from a small region below the antenna. Inspired by the footprint in airborne electromagnetic method, we propose the finite domain (FD) strategy, which limits the calculation of single trace to FD, and combine it with optimized MCW. It can reduce the computational cost of RTM and MCW significantly at the same time, especially for long profile data. Numerical experiments show that the FD reduces the computation by 77.22% with speedup 11.01. The optimized MCW retains the effective information separated from artifacts. The migration of the measured data proves the advantages and practicality of this method in engineering practical exploration.

With the rapid advancements of Big Data and computer vision, many large-scale natural visual datasets are proposed, such as ImageNet-21K, LAION-400M, and LAION-2B. These large-scale datasets significantly improve the robustness and accuracy of models in the natural vision domain. However, the field of medical images continues to face limitations due to relatively small-scale datasets. In this article, we propose a novel method to enhance medical image analysis across domains by leveraging pre-trained models on large natural datasets. Specifically, a Cross-Domain Transfer Module (CDTM) is proposed to transfer natural vision domain features to the medical image domain, facilitating efficient fine-tuning of models pre-trained on large datasets. In addition, we design a Staged Fine-Tuning (SFT) strategy in conjunction with CDTM to further improve the model performance. Experimental results demonstrate that our method achieves state-of-the-art performance on multiple medical image datasets through efficient fine-tuning of models pre-trained on large natural datasets.

Aiming at solving the clutter flooding problem in the traditional cross-correlation reverse time migration (RTM) of ground penetrating radar (GPR), we proposed an improved RTM method based on multi-traces cross-correlation window (MCW) imaging condition. The main difference between the proposed method and the traditional direct stacking is that it can effectively enhance the effective signal while weakening the clutter by performing MC calculation on the single trace imaging results, avoiding the enhancement of both clutter interference and effective signal concurrently by direct stacking. Secondly, the window threshold is set according to the GPR observation accuracy, and the effective signal in the MC result is retained as the abnormal region window, while the imaging results in the non-abnormal region are discarded, so as to suppress the clutter and retain the abnormal region information. Numerical experiments show that, compared with the traditional RTM and total variation de-noising method with cross-correlation imaging conditions, the MCW imaging condition can accurately locate abnormal region, suppress clutter interference, and have the advantage of no loss of effective information, which greatly improves the imaging quality. Finally, the proposed method is applied to the measured data to verify the practicability and effectiveness in practical engineering applications.

For jammer formations, due to advanced digital signal processing and precise synchronization, suppressing and penetrating netted radar systems face more challenges than ever. In this letter, a deep reinforcement learning-based joint path planning and jamming power allocation optimization (DRL-JPPAO) method is proposed. DRL-JPPAO models the penetration operation by a Markov Decision Process and utilizes the Proximal Policy Optimization algorithm to learn optimal sequential actions, which consist of waypoints and jamming power allocation matrices, to solve it. The learning process is guided by a reward function that depends on the success or failure of penetration and the distance to the target. Empirical results with a newly developed environment show that DRL-JPPAO can learn optimal paths and jamming power allocation strategies jointly, accomplishing the penetration operation effectively and elegantly.

Reverse time migration (RTM) has the advantage of high-precision imaging, and it can converge the radar wave back to its actual position, making it widely used in radar exploration. However, there are artifacts, low-frequency noise and fuzzy deep imaging in RTM results. Researchers have proposed full wavefield separation imaging condition and total variation (TV) technique, both of which could suppress noise and artifacts. However, the original wavefield separation method was considerably limited by its extensive calculation, and it cannot solve the problem of weak energy of imaging in the deep zone; the conventional TV technique was likely to be affected by artifacts due to the inevitable over-smoothing-suppression of anomaly edges. To address these issues, this paper improves the RTM methodology by combining an optimized full wavefield separation based on Poynting vector imaging condition and TV-L1 based artifacts suppressing technique. Specifically, the physical significance of the Poynting vector is introduced to separate the wavefield for reducing the calculation burden; the compensation function is integrated with the imaging condition to compensate for the deep energy; the TV-L1 based artifacts suppressing method is used to resolve the imaging problem of loss of specific and edge details. Synthetic data and laboratory data experiments are carried out to verify the effectiveness and practicability of the proposed RTM methodology.

Ground penetrating radar (GPR) is an important shallow electromagnetic non-destructive detection technology. The full waveform inversion (FWI) of GPR data utilizes all information including dynamics and kinematics, theoretically has the highest imaging accuracy, and meets the increasingly sophisticated needs of engineering exploration imaging. However, the bottleneck restricting the FWI is the low calculation efficiency, which cannot meet the requirements of rapid reconstruction of underground medium in actual engineering. In order to improve the calculation efficiency, we introduce the data encoding into the GPR dual-parameter FWI. Data encoding often brings crosstalk noise, and the noise is closely related to the encoding methods and data types. For this reason, we select the encoding of the crosshole data, wide-angle reflection and refraction data, and common-offset data for inversion. Experiments show that data encoding can effectively reduce computing time, and three different GPR data require different encoding methods due to their different redundancies. Total variation (TV) regularization can suppress the noise caused by data encoding. Although it will slightly increase the calculation time, it can significantly improve the inversion quality.

Ground-penetrating radar (GPR) has been regarded as a potentially efficient way of evaluating the growth status of trees and preventing deterioration associated with trunk defects. The majority of current GPR data inversions, however, focused on imaging the macroscale location of defects. As the first attempt to seek a preferable quantitative inversion methodology for specifying tree protection and remedies, this article proposes a full-waveform inversion (FWI) approach involving dual-parameter attributes applied to common-offset GPR data from a commercial antenna. Specifically, the synchronous inversion of both dielectric constant and conductivity improves the identification accuracy of certain defect types. In particular, both a multifrequency strategy and total-variation (TV) regularization are seamlessly introduced to assure inversion stability by overcoming local minima and cycle skipping. Through an irregular trunk model test, the effectiveness of the optimized inversion is initially verified by presenting the precise features of the crack, hollow, and decay with the dual-parameter inversion results. In addition, several other synthetic trunk models and in-site trunk model tests further demonstrate the robustness and practicability of the proposed algorithm, which can offer more specific and comprehensive guidance for the formulation of tree protection and restoration measures.

The full-waveform inversion (FWI) of ground-penetrating radar (GPR) data yields promise for quantitatively characterizing the parameters of the Earth’s shallow subsurface. However, conventional FWI is highly nonlinear and suffers from cycle skipping once the low-frequency data are missed or the initial model is poor. Furthermore, having limited prior knowledge of the subsurface in GPR measurements increases the ill-posedness of the inverse problem. Wavefield reconstruction inversion (WRI), which mitigates cycle skipping, extends the FWI search space by relaxing the wave equation constraint, reduces the nonlinearity, and is less sensitive to the initial model. In this article, we extend WRI to the 2-D frequency-domain imaging of on-ground GPR data. To improve the inversion stability and mitigate the ill-posedness, we utilize modified total variation (MTV) regularization to constrain the inverted models. With a simple numerical example, we first investigate the effects of the penalty parameter, initial models, and MTV regularization on WRI and further discuss the differences between WRI and FWI. Then, we analyze the sensitivity of the proposed approach to the frequency component and noise in a multiple-targets model. Furthermore, we assess our method with a complex synthetic example containing noise-contaminated data, showing that our proposed approach works efficiently even given noisy GPR data. Therefore, the reasonable combination of WRI and MTV regularization can improve the accuracy and efficiency of imaging for on-ground GPR data. This joint approach for the multiparameter quantitative reconstruction of GPR data ultimately exhibits good applicability and strong robustness and is worthy of promotion.