Search Results (15)

Synthetic aperture radar (SAR) recognition systems often need to collect new data and update the network accordingly. However, the network faces the challenge of catastrophic forgetting, where previously learned knowledge might be lost during the incremental learning of new data. To improve the applicability and sustainability of SAR target classification methods, we propose a multi-stage regularization-based class-incremental learning (CIL) method for SAR targets, called SCF-CIL, which addresses catastrophic forgetting. This method offers three main contributions. First, for the feature extractor, we fuse the convolutional neural network features with the scattering center features using a cross-attention feature fusion structure, ensuring both the plasticity and stability of the extracted features. Next, an overfitting training strategy is applied to provide clustering space for unseen classes with an acceptable trade-off in the accuracy of the current classes. Finally, we analyze the influence of training with imbalanced data on the last fully connected layer and introduce a multi-stage regularization method by dividing the calculation of the fully connected layer into three parts and applying regularization to each. Our experiments on SAR datasets demonstrate the effectiveness of these improvements. Full article

This study developed a foamed material with a synergistic microporous-macroporous structure through chemical foaming and high-pressure curing to better utilize the microporous properties of diatomaceous earth in building materials. The effects of different amounts of foaming agent, foam stabilizer, and CaO/SiO₂ on the mechanical properties and pore structure of the samples were investigated. The experimental results demonstrate that, under the influence of the foaming agent, the foam material has developed a multi-stage pore structure that integrates both macropores and micropores. This unique structure results in a dry density range of 467–670 kg/m³, thereby achieving significant material lightweighting. In addition, these macropores enhance the interaction between the micropores of diatomaceous earth and the external environment interface, thereby achieving a balance between the material’s structural stability and functional properties. The material exhibits a porosity of 76.9% and a specific surface area of 42.9 m²/g, while maintaining a high compressive strength of 2.67 MPa. This work provides a technological pathway for the fabrication of multifunctional building materials that have both lightweight and eco-functional properties. Full article

Conventional ship detection using synthetic aperture radar (SAR) is typically limited to fully focused spatial features of the ship target in SAR images. In this paper, we propose a multi-stage feature transfer (MFT)-based reasoning RCNN (MFT-Reasoning RCNN) to detect ships in SAR images. This algorithm can detect the SAR ship target using the MFT strategy and adaptive global reasoning module over all object regions by exploiting diverse knowledge between the ship and its surrounding elements. Specifically, we first calculate the probability of the simultaneous occurrence of environmental and target elements. Then, taking the environmental and target elements as entities, we construct the relationships between them using an adjacency matrix. Finally, we propose an MFT and use filter feature enhancement in the backbone layer to better extract the target features of SAR images and transfer knowledge between datasets. This paper has been tested on more than 10,000 images, and the experimental results demonstrate that our method can effectively detect different-scale ships in SAR images. Full article

Video Synthetic Aperture Radar (ViSAR) operates by continuously monitoring regions of interest to produce sequences of SAR imagery. The detection and tracking of ground-moving targets, through the analysis of their radiation properties and temporal variations relative to the background environment, represents a significant area of focus and innovation within the SAR research community. In this study, some key challenges in ViSAR systems are addressed, including the abundance of low-confidence shadow detections, high error rates in multi-target data association, and the frequent fragmentation of tracking trajectories. A multi-target tracking algorithm for ViSAR that utilizes re-identification (ReID) features and a multi-stage data association process is proposed. The algorithm extracts high-dimensional ReID features using the Dense-Net121 network for enhanced shadow detection and calculates a cost matrix by integrating ReID feature cosine similarity with Intersection over Union similarity. A confidence-based multi-stage data association strategy is implemented to minimize missed detections and trajectory fragmentation. Kalman filtering is then employed to update trajectory states based on shadow detection. Both simulation experiments and actual data processing experiments have demonstrated that, in comparison to two traditional video multi-target tracking algorithms, DeepSORT and ByteTrack, the newly proposed algorithm exhibits superior performance in the realm of ViSAR multi-target tracking, yielding the highest MOTA and HOTA scores of 94.85% and 92.88%, respectively, on the simulated spaceborne ViSAR data, and the highest MOTA and HOTA scores of 82.94% and 69.74%, respectively, on airborne field data. Full article

Deep learning has achieved significant success in various synthetic aperture radar (SAR) imagery interpretation tasks. However, automatic aircraft detection is still challenging due to the high labeling cost and limited data quantity. To address this issue, we propose a multi-stage domain adaptation training framework to efficiently transfer the knowledge from optical imagery and boost SAR aircraft detection performance. To overcome the significant domain discrepancy between optical and SAR images, the training process can be divided into three stages: image translation, domain adaptive pretraining, and domain adaptive finetuning. First, CycleGAN is used to translate optical images into SAR-style images and reduce global-level image divergence. Next, we propose multilayer feature alignment to further reduce the local-level feature distribution distance. By applying domain adversarial learning in both the pretrain and finetune stages, the detector can learn to extract domain-invariant features that are beneficial to the learning of generic aircraft characteristics. To evaluate the proposed method, extensive experiments were conducted on a self-built SAR aircraft detection dataset. The results indicate that by using the proposed training framework, the average precision of Faster RCNN gained an increase of 2.4, and that of YOLOv3 was improved by 2.6, which outperformed other domain adaptation methods. By reducing the domain discrepancy between optical and SAR in three progressive stages, the proposed method can effectively mitigate the domain shift, thereby enhancing the efficiency of knowledge transfer. It greatly improves the detection performance of aircraft and offers an effective approach to address the limited training data problem of SAR aircraft detection. Full article

Terahertz video synthetic aperture radar (THz-ViSAR) has tremendous research and application value due to its high resolution and high frame rate imaging benefits. However, it requires more efficient imaging algorithms. Thus, a novel multistage back projection fast imaging algorithm for the THz-ViSAR system is proposed in this paper to enable continuous playback of images like video. The radar echo data of the entire aperture is first divided into multiple sub-apertures, as with the fast-factorized back projection algorithm (FFBP). However, there are two improvements in sub-aperture imaging. On the one hand, the back projection algorithm (BPA) is replaced by the polar format algorithm (PFA) to improve the sub-aperture imaging efficiency. The imaging process, on the other hand, uses the global Cartesian coordinate system rather than the local polar coordinate system, and the wavenumber domain data of the full aperture are obtained step by step through simple splicing and fusion, avoiding the amount of two-dimensional (2D) interpolation operations required for local polar coordinate system transformation in FFBP. Finally, 2D interpolation for full-resolution images is carried out to image the ground object targets in the same coordinate system due to the geometric distortion caused by linear phase error (LPE) and the mismatch of coordinate systems in different imaging frames. The simulation experiments of point targets and surface targets both verify the effectiveness and superiority of the proposed algorithm. Under the same conditions, the running time of the proposed algorithm is only about 6% of FFBP, while the imaging quality is guaranteed. Full article

A multi-channel synthetic aperture radar (SAR) on board a spaceplane orbiting near the top of the atmosphere is proposed to acquire images of cruising ships. Low pulse repetition frequency (PRF) is required for high-resolution wide-swath (HRWS) imaging, leading to inevitable problems of azimuth spectrum aliasing (ASA) and azimuth Doppler ambiguity (ADA). In this work, we propose a phase matching technique to solve the ASA problem in restoring the azimuth spectrum. A multi-stage compressive-sensing (CS) technique is also proposed to solve both ADA and ASA problems. Five similar types of cruising ship are simulated to verify the efficacy of the proposed approach, at different levels of signal-to-noise ratio. Indices of geometry match, intensity match, and structural similarity are used to identify different ships from the acquired SAR images. Full article

During hydraulic fracturing, the aperture of hydraulic fractures will shrink by the in-situ stress, but will not fully close because of the existence of proppant inside the fracture. In previous studies, few people noticed the existence of proppant, which has resulted in the inaccuracy of simulation results. In this study, based on the boundary element method, a numerical simulation model for sequential fracturing was established, which respectively considered the influence of proppant in staged fracturing and zipper fracturing. In addition, the influence mechanism of proppant on fracture morphology is then revealed. Simulation results show that the residual aperture of the previous hydraulic fracture, which was produced by proppant, may increase with the increase of proppant stiffness and fracture spacing and may also be shrunk by the dynamic propagation of subsequent hydraulic fracture. However, the residual aperture will rebound after hydraulic fracturing construction is finished. The shrinkage and rebound values of residual aperture of hydraulic fracture are usually less than 1 mm. In addition, at the same time, the residual aperture of previous hydraulic fracture may also influence the propagation of subsequent hydraulic fracture. These influences are represented by the bend of fractures in multistage fracturing and the intersection in zipper fracturing. With the increase of well spacing, the influence degree of residual aperture on subsequent fracture propagation is reduced. The previous hydraulic fracture cannot have a significant effect on the deflection of subsequent hydraulic fracture when fracture spacing is between 10 and 30 m. The above research has important guiding significance for controlling fracture morphology in hydraulic fracturing. Full article

A compact high-frequency surface wave radar, used for target detection, suffers from a low signal-to-noise ratio, low detection probability, a high false alarm rate, and low positioning accuracy; this is due to its low transmit power and the reduced aperture size of the receiving antenna array. When target tracking algorithms are applied to compact high-frequency surface wave radar data, track fragmentation often occurs and a long track may be broken into several track segments (a.k.a. tracklets), which degrade the tracking continuity for a maritime surveillance system. We present a multi-stage vessel tracklet association method, based on bidirectional prediction and optimal assignment, to associate the broken tracklets belonging to the same target, and connect them to form one continuous track in a multi-target tracking scenario. Firstly, two global motion parameters, i.e., the average heading and average speed, were, respectively, extracted from the newly initiated and terminated tracklets as features for a rough tracklet association, then k-means clustering was used to produce the preliminary tracklet pairs. Subsequently, the temporal and spatial constraints on the initiated and terminated tracklets were considered to refine the preliminary tracklet pairs, to obtain the candidate tracklet pairs. Finally, the tracklet association costs were calculated using Doppler velocity, range, and azimuth to determine the similarity between tracklets in the candidate tracklet pairs, and an association cost matrix was obtained. Then an optimal assignment method based on Jonker–Volgenant–Castanon algorithm was applied to the association matrix to achieve optimal tracklet matching by minimizing the total association costs. Tracklet association experiments with both simulated and field data were conducted; experimental results show that, compared with existing track segment association methods, the association accuracy of the proposed method is significantly improved with better tracking continuity. Full article

The well spacing for the development of tight reservoirs by multi-stage fracturing is continuously narrowed. Consequently, interwell interference during fracturing is more and more serious, accompanied by a host of issues in fracturing design and oil and gas production. However, the mechanism of interwell interference during fracturing is not explicit. The corresponding laws of the connectivity of rough fractures during fracturing, which plays a critical role in interwell interference, are not fully understood. In this study, on the basis of characterizing the roughness of fractures, a laboratory evaluation method for fracture connectivity was established. The connectivity characteristics of rough fractures and factors affecting the fracture connectivity are studied. The time and scale effects of fracture connectivity were discussed and their application in interwell interference was analyzed. The results show that the connectivity performance of rough fractures can be characterized by the time for pressure decay. The upstream pressure gradually decreases over time, and the decline rate is related to the fracture aperture, the fracture surface roughness, the contact area of the closed fractures, and liquid properties. Specifically, the decrease in fracture aperture and the increase in fluid viscosity leads to a significant reduction in fracture connectivity. While larger fracture surface roughness and contact area can make fracture connectivity better. The connectivity of the fracture system is one of the significant mechanisms causing interwell interference during fracturing. The connectivity of rough fractures formed during fracturing has remarkable scale and time effects. This study can effectively guide the fracturing design and the evaluation of the impact of fracture connectivity on production. Full article

As a by-product of Interferometric Synthetic Aperture Radar (SAR, InSAR) technique, interferometric coherence is a measure of the decorrelation noise for InSAR observation, where the lower the coherence value, the more serious the decorrelation noise. In the densely vegetated area, the coherence value could be too low to obtain any valuable signals, leading to the degradation of InSAR performance and the possible waste of expensive SAR data. Normalized Difference Vegetation Index (NDVI) value is a measure of the vegetation coverage and can be estimated from the freely available optical satellite images. In this paper, a multi-stage model is established to quantitatively estimate the decorrelation noise for vegetable areas based on Landsat-derived NDVI prior to the acquisition of SAR data. The modeling process is being investigated with the L-band ALOS-1/PALSAR-1 data and the Landsat-5 optical data acquired in the Meitanba area of Hunan Province, China. Furthermore, the reliability of the established model is verified in the Longhui area, which is situated near the Meitanba area. The results demonstrate that the established model can quantitatively estimate InSAR decorrelation associated with the vegetation coverage. Full article

Multiphoton Quantum Key Distribution (QKD) has recently been proposed to exchange the secret keys using the rotational of polarization over a multi-stage protocol. It has the ability to outperform the weaknesses of a single photon QKD by improving the generation of key rate and distance range. This paper investigates the theoretical aspects of multiphoton QKD protocol’s performance over free space optic (FSO) networks. The most common setup for quantum communication is the single-beam approach. However, the single-beam setup has limitations in terms of high geometrical loss. In this paper, the symmetry multiple-beam for quantum communication which is called as Multiphoton Quantum Communication-Multiple Beam (MQC-MB) is proposed to transmit the multiphoton from the sender to the receiver in order to minimize the impact of geometrical loss that is faced by the single-beam setup. The analysis was carried out through mathematical analysis by establishing the FSO quantum model with the effects of atmospheric and geometrical loss as well as considering atmospheric turbulence modeled by log-normal distribution. The design criteria of FSO, such as the transmitter, receiver, beam divergence, and diameter of apertures, are analytically investigated. The numerical results demonstrate that the MQC-MB outperforms the single-beam in terms of reducing channel loss by about 8 dB and works well under strong turbulence channel. Furthermore, the MQC-MB reduces the quantum bit error rate (QBER) and improves the secret key rate (SKR) as compared to the single-beam system even though the distance between the sender and receiver increases. Full article

In synthetic aperture radar (SAR) images, ships are often arbitrary-oriented and densely arranged in complex backgrounds, posing enormous challenges for ship detection. However, most existing methods detect ships with horizontal bounding boxes, which leads to the redundancy of detected regions. Furthermore, the high Intersection-over-Union (IoU) between two horizontal bounding boxes of densely arranged ships can cause missing detection. In this paper, a multi-stage rotational region based network (MSR2N) is proposed to solve the above problems. In MSR2N, the rotated bounding boxes, which can reduce background noise and prevent missing detection caused by high IoUs, are utilized to represent ship regions. MSR2N consists of three modules: feature pyramid network (FPN), rotational region proposal network (RRPN), and multi-stage rotational detection network (MSRDN). First of all, the FPN is applied to combine high-resolution features with semantically strong features. Second, in RRPN, a rotation-angle-dependent strategy is employed to generate multi-angle anchors which can represent arbitrary-oriented ship regions more felicitously than horizontal anchors. Finally, the MSRDN with three sub-networks is proposed to regress proposals of ship regions stage by stage. Meanwhile, the incrementally increasing IoU thresholds are selected for resampling positive and negative proposals in sequential stages of MSRDN, which eliminates close false positive proposals successively. With the above characteristics, MSR2N is more suitable and robust for ship detection in SAR images. The experimental results on SAR ship detection dataset (SSDD) show that the MSR2N has achieved state-of-the-art performance. Full article

Multi-sensor satellite imagery data promote fast, cost-efficient regional geological mapping that constantly forms a criterion for successful gold exploration programs in harsh and inaccessible regions. The Barramiya–Mueilha sector in the Central Eastern Desert of Egypt contains several occurrences of shear/fault-associated gold-bearing quartz veins with consistently simple mineralogy and narrow hydrothermal alteration haloes. Gold-quartz veins and zones of carbonate alteration and listvenitization are widespread along the ENE–WSW Barramiya–Um Salatit and Dungash–Mueilha shear belts. These belts are characterized by heterogeneous shear fabrics and asymmetrical or overturned folds. Sentinel-1, Phased Array type L-band Synthetic Aperture Radar (PALSAR), Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel-2 are used herein to explicate the regional structural control of gold mineralization in the Barramiya–Mueilha sector. Feature-oriented Principal Components Selection (FPCS) applied to polarized backscatter ratio images of Sentinel-1 and PALSAR datasets show appreciable capability in tracing along the strike of regional structures and identification of potential dilation loci. The principal component analysis (PCA), band combination and band ratioing techniques are applied to the multispectral ASTER and Sentinel-2 datasets for lithological and hydrothermal alteration mapping. Ophiolites, island arc rocks, and Fe-oxides/hydroxides (ferrugination) and carbonate alteration zones are discriminated by using the PCA technique. Results of the band ratioing technique showed gossan, carbonate, and hydroxyl mineral assemblages in ductile shear zones, whereas irregular ferrugination zones are locally identified in the brittle shear zones. Gold occurrences are confined to major zones of fold superimposition and transpression along flexural planes in the foliated ophiolite-island arc belts. In the granitoid-gabbroid terranes, gold-quartz veins are rather controlled by fault and brittle shear zones. The uneven distribution of gold occurrences coupled with the variable recrystallization of the auriferous quartz veins suggests multistage gold mineralization in the area. Analysis of the host structures assessed by the remote sensing results denotes vein formation spanning the time–space from early transpression to late orogen collapse during the protracted tectonic evolution of the belt. Full article

Among the objectives of the upcoming space missions Tandem-L and BIOMASS, is the 3-D representation of the global forest structure via synthetic aperture radar (SAR) tomography (TomoSAR). To achieve such a goal, modern approaches suggest solving the TomoSAR inverse problems by exploiting polarimetric diversity and structural model properties of the different scattering mechanisms. This way, the related tomographic imaging problems are treated in descriptive regularization settings, applying modern non-parametric spatial spectral analysis (SSA) techniques. Nonetheless, the achievable resolution of the commonly performed SSA-based estimators highly depends on the span of the tomographic aperture; furthermore, irregular sampling and non-uniform constellations sacrifice the attainable resolution, introduce artifacts and increase ambiguity. Overcoming these drawbacks, in this paper, we address a new multi-stage iterative technique for feature-enhanced TomoSAR imaging that aggregates the virtual adaptive beamforming (VAB)-based SSA approach, with the wavelet domain thresholding (WDT) regularization framework, which we refer to as WAVAB (WDT-refined VAB). First, high resolution imagery is recovered applying the descriptive experiment design regularization (DEDR)-inspired reconstructive processing. Next, the additional resolution enhancement with suppression of artifacts is performed, via the WDT-based sparsity promoting refinement in the wavelet transform (WT) domain. Additionally, incorporation of the sum of Kronecker products (SKP) decomposition technique at the pre-processing stage, improves ground and canopy separation and allows for the utilization of different better adapted TomoSAR imaging techniques, on the ground and canopy structural components, separately. The feature enhancing capabilities of the novel robust WAVAB TomoSAR imaging technique are corroborated through the processing of airborne data of the German Aerospace Center (DLR), providing detailed volume height profiles reconstruction, as an alternative to the competing non-parametric SSA-based methods. Full article