2025
2025
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Record 25 of
Title:Surface hardness prediction for laser shock peening using narrow-band MCP-PMT and deep feature fusion with key elements and key frames
Author Full Names:Du, Zhengyao(1,2); Zhang, Zhifen(1,2); Qin, Rui(1,2); Xiang, Xianwen(1,2); Li, Shaohui(3); Su, Yu(1,2); Wen, Guangrui(1,2); He, Weifeng(1,2); Chen, Xuefeng(1,2)Source Title:Journal of Manufacturing ProcessesLanguage:EnglishDocument Type:Journal article (JA)Abstract:Laser shock peening (LSP) is a surface modification technology that significantly enhance the mechanical properties of materials, commonly improving the fatigue life of critical components such as aero engine blades. However, the high sampling rate of laser-induced plasma spectra and the complexity of physical information challenge spatio-temporal resolution, affecting the stability and consistency of LSP quality monitoring. This study address these challenges by leverageing the ultra-high sampling rate of Narrowband Microchannel Plate Photomultiplier (Nb-MCP-PMT, NMP) GHz to monitor rapid and weak light signals from laser-induced plasma in LSP process. High-speed photography elucidates the physical mechanisms of plasma evolution behind the NMP signal, showing that the 0–1.2 μs period in the NMP signal defines the dynamic balance stage, associated with plasma generation and expansion, while the 1.2 μs–1200 μs period corresponds to plasma diffusion and decay, defined as the attenuation stage of the NMP signal. Key features such as NMP-Min, NMP-Median, NMP-Std, NMP-ADif, NMP-Ske, and NMP-Kur were extracted to capture the periodicity, trend, and non-stationarity of NMP signals. A Transformer-Fusion-Attention mechanism (TRM-F-AM) model was developed to automatically extract temporal depth information and identify key features and frames. Distinct attention weight distributions were observed during the dynamic balance and slow attenuation stages of the NMP signal. The feature set redundancy was minimized, marking critical time periods for LSP process monitoring, specifically frames 1–80 during the dynamic balance stage and frames 155–265 during the slow attenuation stage. The model achieved a prediction accuracy of 99.05 % for LSP surface hardness on TC4 and 7075Al targets, surpassing TCN, LSTM, 1D-CNN, and ensemble learning models. ? 2025Affiliations:(1) National Key Lab of Aerospace Power System and Plasma Technology, Xi'an Jiaotong University, Xi'an; 710049, China; (2) School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an; 710049, China; (3) Key Laboratory of Ultra-fast Photoelectric Diagnostics Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, ChinaPublication Year:2025Volume:136Start Page:228-245DOI Link:10.1016/j.jmapro.2025.01.005數(shù)據(jù)庫ID（收錄號）:20250517786456 -
Record 26 of
Title:Comparative analysis of optical gating time characterization methods for ultrafast gated image intensifiers with sub-nanosecond temporal resolution
Author Full Names:Yang, Yang(1,2); Gou, Yongsheng(1); Feng, Penghui(1,3); Xu, Yan(1); Wang, Bo(1); Liu, Baiyu(1); Tian, Jinshou(1,2); Wang, Xu(1); Liu, Hengbo(1)Source Title:Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated EquipmentLanguage:EnglishDocument Type:Journal article (JA)Abstract:Ultrafast gated image intensifiers are crucial for capturing high-resolution images with sub-nanosecond temporal resolution. This study compares two methods for characterizing optical gating time in these intensifiers: Method I, involving a "laser pulse walkthrough," and Method II, utilizing a fiber optic delay array. Both methods were applied to the same intensifier, with results analyzed for consistency and discrepancies. The findings indicate that both methods yield consistent optical gating times within their standard deviations range, though Method II exhibited greater dispersion at the photocathode edges due to spatial non-uniformity. The study further reveals that while the optical gating time distribution across the photocathode is uniformly consistent, a constant delay exists between different regions. To enhance measurement precision it is recommended to subdivide the photocathode for individual calibration of gating time and delay. These insights are pivotal for improving the precision and reliability of optical gating time measurements in applications requiring sub-nanosecond or even picosecond temporal resolution. ? 2024 Elsevier B.V.Affiliations:(1) Key Laboratory of Ultrafast Photoelectric Diagnostic Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China; (2) Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan; 030006, China; (3) School of Electronic Science and Engineering, Xi'an Jiaotong University, Xi'an; 710049, ChinaPublication Year:2025Volume:1070Article Number:170023DOI Link:10.1016/j.nima.2024.170023數(shù)據(jù)庫ID（收錄號）:20244417287989 -
Record 27 of
Title:A data processing method for multispectral emissivity and temperature with the introduction of new objective function and nonlinear constraints
Author Full Names:Yang, Longjie(1,2); Bai, Yonglin(1); Zheng, Jinkun(1); Wang, Bo(1)Source Title:Optics CommunicationsLanguage:EnglishDocument Type:Journal article (JA)Abstract:The underdetermined equation in multispectral pyrometer temperature measurement involves simultaneous unknowns of emissivity and temperature, posing a challenging obstacle to achieving accurate temperature inversion. In recent years, constrained optimization algorithms have been increasingly employed to address this issue. However, these algorithms need to set the appropriate initial emissivity values in particular and the imposition of manual constraints on the search range for emissivity. In this paper, a new data processing method that does not require these artificial Settings is proposed. Our method incorporates new objective functions and nonlinear constraints into the inversion of multispectral emissivity and temperature, while employing the Barrier Function Interior Point Method as an optimization tool. In addition, it has to be mentioned that in the blackbody temperature setting of the reference temperature model, the temperature of the blackbody is set very close to the target temperature by the constrained optimization algorithm, which obviously does not meet the needs of large-scale temperature measurement. The data processing method proposed in this paper addresses situations where there is a significant difference between the blackbody set temperature and the target temperature, ensuring both accuracy and speed over a wide range. Experiments demonstrate that our proposed method achieves a relative error of less than 0.42% in emissivity inversion, less than 0.57% in temperature inversion, and a calculation time of under 0.2 s. Our method can be applied to some high-precision and fast temperature measurement occasions that require short processing time and small relative error. ? 2024 Elsevier B.V.Affiliations:(1) Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, 17 Information Avenue, Xi'an; 710119, China; (2) University of Chinese Academy of Sciences, 1 Yanqihu East Rd, Huairou District, Beijing; 101408, ChinaPublication Year:2025Volume:576Article Number:131311DOI Link:10.1016/j.optcom.2024.131311數(shù)據(jù)庫ID（收錄號）:20244817437266 -
Record 28 of
Title:Leveraging central-surrounding receptive fields for single image dehazing
Author Full Names:Tang, Zhifeng(1,2); Zhang, Yunyao(1); Zhang, Peng(3); Dong, Wenkang(1); Zhang, Zhiyong(1); Zhang, Xingguang(1); Zhang, Ning(4)Source Title:NeurocomputingLanguage:EnglishDocument Type:Journal article (JA)Abstract:Image dehazing is a representative low-level vision task that estimates latent haze-free images from hazy images. In recent years, Vision Transformers (ViT) have shown promising dehazing performance, leveraging their capacity for global perception through long-sequence dependencies in cost of high resource consumption. Therefore, our approach seeks to integrate the utilization of global information into the Convolutional Neural Network (CNN) framework in a more resource-efficient manner. In this paper, we introduce the Adaptive Center-Surround Receptive Field (ACSRF) network architecture inspired by the central-peripheral receptive field in biological vision for single-image haze removal. This leads to a unique receptive field mechanism that effectively combines both central and surrounding information. Our ACRSF addresses this by initially compressing global information and then merging it within the CNN, significantly boosting the capability to integrate local and global information, and effectively handling dominant color tones. Experimental results on four publicly available real-world image dehazing datasets show that our ACRSF outperforms current state-of-the-art methods in recovering global information, especially in dominant color tones. Importantly, this technology demonstrates its effectiveness in realistic scenarios, contributing significantly to improving traffic safety in adverse weather conditions. The code is available at https://github.com/JavanTang/ACSRF. ? 2024 Elsevier B.V.Affiliations:(1) School of Information Science and Technology, Northwest University, Xi'an; 710127, China; (2) China Electronics Technology Group Corp 20th Research Institute, Xi'an; 710071, China; (3) School of Computer Science, Northwestern Polytechnical University, Xi'an; 710129, China; (4) Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an; 710119, ChinaPublication Year:2025Volume:618Article Number:129075DOI Link:10.1016/j.neucom.2024.129075數(shù)據(jù)庫ID（收錄號）:20245117528884 -
Record 29 of
Title:Simulation investigation on the pulse/analog dual-mode electron multiplier with discrete arc-shaped dynodes
Author Full Names:Liu, Li(1); Li, Jie(1); Liu, Biye(1); Wang, Teng(1); Liu, Hulin(2); Yun, Xintuan(1); Wu, Shengli(1,3); Hu, Wenbo(1,3)Source Title:Journal of Vacuum Science and Technology BLanguage:EnglishDocument Type:Journal article (JA)Abstract:To satisfy the demand of mass spectrometers for high sensitivity and high resolution ion detection, a type of pulse/analog dual-mode, arc-shaped, discrete-dynode electron multiplier (DM-ADD-EM) with 20-stage dynode structure was proposed, and its gain and time characteristics were investigated by three-dimensional numerical simulation. Each of the 2nd-20th dynodes has an arc-shaped substrate consisting of a long arc segment and a short arc segment, attached with a pair of side baffles. The simulation results indicate that the two side baffles play a role in focusing the electron beam to the central regions between them, reducing the number of secondary electrons escaping from the dynode array and, therefore, raising the electron collection efficiency of dynodes. As the radius (R) of arc-shaped substrates increases, the device gain rises. In the case of the 3.6-mm R, there is an optimum long-arc-segment center angle (α = 79°) at which the DM-ADD-EM reaches relatively high analog gain and pulse gain together with preferable time response, and its dynodes in the pulse section can be better protected from electron impact in analog output mode. In addition, the long-arc-segment center angle of the 12th-17th dynodes was further optimized to 84° for suppressing ion feedback. A dynode-configuration-optimized DM-ADD-EM with SiO2-doped MgO-Au secondary electron emission film achieves a pulse gain of 7.2 × 108, an analog gain of 1.3 × 104, a pulse rise time of 3.8 ns, and a pulse width of 9.2 ns under the analog-section/pulse-section voltages of ?1800 V/1000 V, exhibiting significantly improved pulse gain and better time response. These results provide a basis for the design and fabrication of high-performance EMs. ? 2025 Author(s).Affiliations:(1) Key Laboratory for Physical Electronics and Devices, the Ministry of Education, School of Electronic Science and Engineering, State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University, No. 28, Xianning West Road, Xi’an; 710049, China; (2) Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, No. 17, Xinxi Road, Xi’an; 710119, China; (3) Moe Key Laboratory for Multifunction Materials and Structure, School of Electronic Science and Engineering, Xi'an Jiaotong University, No. 28, Xianning West Road, Xi’an; 710049, ChinaPublication Year:2025Volume:43Issue:1Article Number:012201DOI Link:10.1116/6.0004105數(shù)據(jù)庫ID（收錄號）:20250417730905 -
Record 30 of
Title:Global calibration method for multi-view-based vibration measurement of large structures
Author Full Names:Lv, Junhao(1); Yao, Dong(2); Guo, Yuan(3); Xie, Junwei(1); Xiao, Jinyou(1); Yang, Lu(1,4)Source Title:Measurement: Journal of the International Measurement ConfederationLanguage:EnglishDocument Type:Journal article (JA)Abstract:A multi-view global calibration method is proposed to overcome the limitations of overlapping regions in multi-view structural vibration measurement. This method establishes a mapping relationship from image points to experimental model points at the outset of vibration measurement, enabling a one-time global calibration of multi-view vibration response data. Furthermore, an image point detection method based on shape-function matching is designed to improve global calibration accuracy. Experimental results demonstrate that the proposed method excels in point detection and global calibration. Finally, the feasibility of this method is validated through vibration experiments on large aircraft fuselage structures. ? 2024Affiliations:(1) School of Astronautics, Northwestern Polytechnical University, Xi'an; 710072, China; (2) Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China; (3) Hiwing Materials Co., Ltd, China Aerospace Science & Industry Corp, Jiangsu; 212132, China; (4) Shaanxi FAST Gear Co., Ltd, Xi'an; 710119, ChinaPublication Year:2025Volume:242Article Number:115809DOI Link:10.1016/j.measurement.2024.115809數(shù)據(jù)庫ID（收錄號）:20244017137614 -
Record 31 of
Title:Degradation-aware deep unfolding network with transformer prior for video compressive imaging
Author Full Names:Yin, Jianfu(1,2,3); Wang, Nan(4); Hu, Binliang(1,3); Wang, Yao(5); Wang, Quan(1,3)Source Title:Signal ProcessingLanguage:EnglishDocument Type:Journal article (JA)Abstract:In video snapshot compressive imaging (SCI) systems, video reconstruction methods are used to recover spatial–temporal-correlated video frame signals from a compressed measurement. While unfolding methods have demonstrated promising performance, they encounter two challenges: (1) They lack the ability to estimate degradation patterns and the degree of ill-posedness from video SCI, which hampers guiding and supervising the iterative learning process. (2) The prevailing reliance on 3D-CNNs in these methods limits their capacity to capture long-range dependencies. To address these concerns, this paper introduces the Degradation-Aware Deep Unfolding Network (DADUN). DADUN leverages estimated priors from compressed frames and the physical mask to guide and control each iteration. We also develop a novel Bidirectional Propagation Convolutional Recurrent Neural Network (BiP-CRNN) that simultaneously captures both intra-frame contents and inter-frame dependencies. By plugging BiP-CRNN into DADUN, we establish a novel end-to-end (E2E) and data-dependent deep unfolding method, DADUN with transformer prior (TP), for video sequence reconstruction. Experimental results on various video sequences show the effectiveness of our proposed approach, which is also robust to random masks and has wide generalization bounds. ? 2024 Elsevier B.V.Affiliations:(1) Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China; (2) University of Chinese Academy of Sciences, Beijing; 100049, China; (3) The Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an; 710119, China; (4) School of Information Science and Technology, Hainan Normal University, Haikou; 571158, China; (5) Center for Intelligent Decision-making and Machine Learning, Xi'an Jiaotong University, Xi'an; 7100499, ChinaPublication Year:2025Volume:227Article Number:109660DOI Link:10.1016/j.sigpro.2024.109660數(shù)據(jù)庫ID（收錄號）:20243717012813 -
Record 32 of
Title:Characterization of ultrasonic vibration and polishing force in sapphire ultrasonic vibration-assisted flexible polishing: Insights from in-situ monitoring systems
Author Full Names:Geng, Ying(1); Sun, Guoyan(2,3); Wang, Sheng(1); Zhao, Qingliang(1)Source Title:UltrasonicsLanguage:EnglishDocument Type:Journal article (JA)Abstract:Sapphire ultrasonic vibration-assisted flexible polishing (UVAFP) is a promising technique for comprehensively improving the surface integrity of machined parts. The technique was performed on an ultra-precision machine tool with the in-situ monitoring systems in this paper, which aims to provide a new perspective for understanding the material removal mechanisms in the sapphire UVAFP process. A Taguchi L9 (43) orthogonal experiment was conducted to investigate the effects of feed distance, spindle speed, ultrasonic vibration (UV), and polishing time on the surface finish and material removal in the process. In addition, the effect of a polyurethane ball tool is not trivial. A single-factor experiment was conducted for exploring it. Based on a laser displacement measurement system and an acoustic emission sensor system, the characteristics of time-dependent ultrasonic amplitude and ultrasonic frequency for the sapphire UVAFP system were analyzed, with the effectiveness of UV demonstrated. Based on a three-component force measurement system, the characteristics of normal force and its relationship with process parameters and tool deformation were analyzed, with macro- and micro-level examined. In conclusion, this paper presents the characterization of UV and polishing force in the sapphire UVAFP process, providing novel insights into understanding the material removal mechanisms of sapphire and even more manufacturing problems. ? 2024 Elsevier B.V.Affiliations:(1) School of Mechatronics Engineering, Harbin Institute of Technology, Harbin; 150001, China; (2) Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China; (3) College of Artificial Intelligence, National University of Defense Technology, Changsha; 410003, ChinaPublication Year:2025Volume:145Article Number:107431DOI Link:10.1016/j.ultras.2024.107431數(shù)據(jù)庫ID（收錄號）:20243717018445 -
Record 33 of
Title:Corrigendum to "Opto-mechanical-thermal integration design of the primary optical system for a tri-band aviation camera" [Measure. PE 242 (2025) 116319] (Measurement (2025) 242(PE), (S0263224124022048), (10.1016/j.measurement.2024.116319))
Author Full Names:Zhang, Kailin(1); Pan, Yue(1); Xu, Xiping(1); Xu, Liang(2); Liu, Wancheng(3); Hu, Motong(1); Lu, Yi(1); Cao, Yajie(1)Source Title:Measurement: SensorsLanguage:EnglishDocument Type:Erratum (ER)Abstract:The authors regret that the funding details were omitted from the main manuscript. The correct funding information is as follows: The research was funded by Jilin Scientific and Technological Development Program (Grant No. 20230201052GX) and 111 Project (Grant No. D21009). The authors would like to apologise for any inconvenience caused. ? 2024 The Author(s)Affiliations:(1) Changchun University of Science and Technology, No. 7186, Weixing Road, Jilin Province, Changchun; 130022, China; (2) Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Shaanxi, Xi'an; 710119, China; (3) National Key Laboratory of Electromagnetic Space Security, ChinaPublication Year:2025Volume:37Article Number:101804DOI Link:10.1016/j.measen.2024.101804數(shù)據(jù)庫ID（收錄號）:20250217653567 -
Record 34 of
Title:A context constraint and sparse learning based on correlation filter for high-confidence coarse-to-fine visual tracking
Author Full Names:Su, Yinqiang(1,2); Xu, Fang(2); Wang, Zhongshi(2); Sun, Mingchao(2); Zhao, Hui(1)Source Title:Expert Systems with ApplicationsLanguage:EnglishDocument Type:Journal article (JA)Abstract:Discriminative Correlation Filters (DCFs) have recently garnered significant considerable in the field of visual single tracking. However, existing trackers frequently struggle to fully mine the structural complementarity and diversity among various features, resulting in a decline in discriminability in complex scenarios. To address these challenges, we explore a novel context-aware sparse learning (CCSL) framework based on DCFs and hierarchically infer the maximum response represented by each class filter to locate the target confidently. Guided by sparse learning principles, our tracker collaboratively and bidirectionally selects effective spatial elements that encode target appearance through lasso regression and sparse response estimate. Additionally, the target and its surroundings are jointly incorporated into the learning framework, thereby bolstering the discriminability of the learned model. To tackle the optimization problem, we leverage the Alternating Direction Method of Multipliers (ADMM) in the Fourier domain. Furthermore, we introduce a hierarchical inference scheme for target localization that harnesses complementary cues from different features, which leverages historical displacement and multi-modal detection to reveal the tracking state respectively. Extensive experiments are conducted on renowned benchmarks, including OTB-100, UAV123, UAV20L, and TC128, to demonstrate the efficiency and effectiveness of the proposed tracker. These results underscore the tracker's ability to perform robustly in complex scenarios, showcasing its superior discriminative power and accuracy. ? 2024 Elsevier LtdAffiliations:(1) Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China; (2) State Key Laboratory of Dynamic Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun; 130033, ChinaPublication Year:2025Volume:268Article Number:126225DOI Link:10.1016/j.eswa.2024.126225數(shù)據(jù)庫ID（收錄號）:20250117622614 -
Record 35 of
Title:Opto-mechanical-thermal integration design of the primary optical system for a tri-band aviation camera
Author Full Names:Zhang, Kailin(1); Pan, Yue(1); Xu, Xiping(1); Xu, Liang(2); Liu, Wancheng(3); Hu, Motong(1); Lu, Yi(1); Cao, Yajie(1)Source Title:Measurement: Journal of the International Measurement ConfederationLanguage:EnglishDocument Type:Journal article (JA)Abstract:This paper presents a tri-band aviation camera that integrates short-wave infrared (SWIR, 0.9–1.7 μm), mid-wave infrared (MWIR, 3.7–4.8 μm) and visible (VIS, 0.486–0.656 μm) bands into a primary optical system and three subsystems, to enhance optical remote sensing capabilities. The focus is on opto-mechanical-thermal integration to effectively manage thermal stress and minimise deformation. Finite Element Analysis is employed to extract Zernike coefficients for deformation analysis, facilitating a comprehensive assessment of the camera's performance across a temperature range of ?40 °C to 60 °C. An innovative flexible support system is integrated to maintain the optimal surface figure of the primary mirror, further reducing thermal effects. Extensive empirical testing, including resolution and wavefront error detection, has validated the system's robustness under various thermal conditions. ? 2024 The Author(s)Affiliations:(1) Changchun University of Science and Technology, No. 7186, Weixing Road, Jilin Province, Changchun; 130022, China; (2) Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Shaanxi, Xi'an; 710119, China; (3) National Key Laboratory of Electromagnetic Space Security, ChinaPublication Year:2025Volume:242Article Number:116319DOI Link:10.1016/j.measurement.2024.116319數(shù)據(jù)庫ID（收錄號）:20244817447962 -
Record 36 of
Title:Multiscale Adaptively Spatial Feature Fusion Network for Spacecraft Component Recognition
Author Full Names:Zhang, Wuxia(1); Shao, Xiaoxiao(1); Mei, Chao(2); Pan, Xiaoying(1); Lu, Xiaoqiang(3)Source Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingLanguage:EnglishDocument Type:Journal article (JA)Abstract:Spacecraft component recognition is crucial for tasks such as on-orbit maintenance and space docking, aiming to identify and categorize different parts of a spacecraft. Semantic segmentation, known for its excellence in instance-level recognition, precise boundary delineation, and enhancement of automation capabilities, is well-suited for this task. However, applying existing semantic segmentation methods to spacecraft component recognition still encounters issues with false detections, missed detections, and unclear boundaries of spacecraft components. In order to address these issues, we propose a multiscale adaptively spatial feature fusion network (MASFFN) for spacecraft component recognition. The MASFFN comprises a spatial attention-aware encoder (SAE) and a multiscale adaptively spatial feature fusion-based decoder (Multi-ASFFD). First, the spatial attention-aware feature fusion module within the SAE integrates spatial attention-aware features, mid-level semantic features, and input features to enhance the extraction of component characteristics, thus improving the accuracy in capturing size, shape, and texture information. Second, the multi-scale adaptively spatial feature fusion module within the Multi-ASFFD cascades four adaptively spatial feature fusion blocks to fuse low-level, middle-level, and high-level features at various scales to enrich the semantic information for different spacecraft components. Finally, a compound loss function comprising the cross-entropy and boundary losses is presented to guide the MASFFN better focus on the unclear component edge. The proposed method has been validated on the UESD and URSO datasets, and the experimental results demonstrate the superiority of MASFFN over existing spacecraft component recognition methods. ? 2008-2012 IEEE.Affiliations:(1) Xi'an University of Posts and Telecommunications, Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, School of Computer Science and Technology, Xi'an; 710121, China; (2) Chinese Academy of Sciences, Center for Optical Imagery Analysis and Learning, Xi'an Institute of Optics and Precision Mechanics, Xi'an; 710119, China; (3) Fuzhou University, College of Physics and Information Engineering, Fuzhou; 350108, ChinaPublication Year:2025Volume:18Start Page:3501-3513DOI Link:10.1109/JSTARS.2024.3523273數(shù)據(jù)庫ID（收錄號）:20250417745333