Ship detection via spaceborne synthetic aperture radar(SAR)has become a research hotspot.However,existing small ship detection methods based on the radar signal domain and SAR image features cannot obtain highly accur...Ship detection via spaceborne synthetic aperture radar(SAR)has become a research hotspot.However,existing small ship detection methods based on the radar signal domain and SAR image features cannot obtain highly accurate results because of the obvious coherent speckle noise at sea and strong reflection interference from near‑shore objects.To resolve the above problems,this study proposes a dual‑domain joint dense multiple small ship target detection method for spaceborne SAR image that simultaneously detects objects in the image and frequency domains.This method uses an attention mechanism module and algorithm structure adjustments to improve the small ship target feature mining ability.In the frequency‑based image generation,extreme signal strength values are detected in the azimuth and range directions,with the results of the two complementing each other to realize dual‑domain joint small ship target detection.The comprehensive qualitative and quantitative evaluation results show that the proposed method can attain a final precision rate of 92.25%and achieve accurate results for SAR ship detection in open‑sea,coastal,and port area ships.The test results for the self‑built SAR small‑ship dataset demonstrate the effectiveness and universality of the method.展开更多
Background: Rheumatoid arthritis is a form of autoimmune disease characterized by synovitis that can ultimately cause joint deformities and impaired functioning. The cartilage destruction is one of the most important ...Background: Rheumatoid arthritis is a form of autoimmune disease characterized by synovitis that can ultimately cause joint deformities and impaired functioning. The cartilage destruction is one of the most important indicators for diagnosis and treatment of rheumatoid arthritis, and it is radiographically manifested as joint space narrowing. Issue: In the literature, the joint space narrowing progression between a baseline and its follow-up finger joint images can be quantified by using image registration algorithm. We found that the inconsistencies of joint angles may lead to characteristic mismatches and thus severely affect the accuracy of joint space narrowing quantifications. Methods: In this work, we introduce a rotation invariant phase only correlation in joint space narrowing quantification for the joint angle correction. Further, we propose a confidence index to quantify the quantification reliability of phase only correlation based on phase dispersion in phase difference spectrum. Conclusion: In our clinical experiments, the proposed quantification method can effectively overcome and manage the mismatch due to the inconsistency of joint angles. Additionally, the confidence index shows a high consistency with the joint space narrowing progression examinations manually done by a trained radiologist and one radiological technologist.展开更多
瞄准制衡强敌“马赛克战”“决策中心战”等新技术驱动的作战概念及威胁挑战,聚焦未来跨域联合作战指挥控制(command and control,C2)全流程决策需求,遵循平行智能理论框架,提出了基于筹划-准备-执行-评估(planning-readiness-execution...瞄准制衡强敌“马赛克战”“决策中心战”等新技术驱动的作战概念及威胁挑战,聚焦未来跨域联合作战指挥控制(command and control,C2)全流程决策需求,遵循平行智能理论框架,提出了基于筹划-准备-执行-评估(planning-readiness-execution-assessment,PREA)环与观察-判断-决策-行动(observe-orient-decide-act,OODA)环的平行指挥控制与管理(command&control and management,C2M)新范式,以期实现智能机器辅助指挥员作战全流程的分层次、个性化决策支持,减轻指挥员认知负担、降低决策复杂度,实现机器对指挥员“人脑”的智能扩展与增强,为塑造全局决策优势提供牵引和支撑。展开更多
基金supported by the Foundation Strengthening Fund Project(No.2021-JCJQ-JJ0251)in part by the National Natural Science Foundation of China(Nos.42301384 and 42271448)。
文摘Ship detection via spaceborne synthetic aperture radar(SAR)has become a research hotspot.However,existing small ship detection methods based on the radar signal domain and SAR image features cannot obtain highly accurate results because of the obvious coherent speckle noise at sea and strong reflection interference from near‑shore objects.To resolve the above problems,this study proposes a dual‑domain joint dense multiple small ship target detection method for spaceborne SAR image that simultaneously detects objects in the image and frequency domains.This method uses an attention mechanism module and algorithm structure adjustments to improve the small ship target feature mining ability.In the frequency‑based image generation,extreme signal strength values are detected in the azimuth and range directions,with the results of the two complementing each other to realize dual‑domain joint small ship target detection.The comprehensive qualitative and quantitative evaluation results show that the proposed method can attain a final precision rate of 92.25%and achieve accurate results for SAR ship detection in open‑sea,coastal,and port area ships.The test results for the self‑built SAR small‑ship dataset demonstrate the effectiveness and universality of the method.
文摘Background: Rheumatoid arthritis is a form of autoimmune disease characterized by synovitis that can ultimately cause joint deformities and impaired functioning. The cartilage destruction is one of the most important indicators for diagnosis and treatment of rheumatoid arthritis, and it is radiographically manifested as joint space narrowing. Issue: In the literature, the joint space narrowing progression between a baseline and its follow-up finger joint images can be quantified by using image registration algorithm. We found that the inconsistencies of joint angles may lead to characteristic mismatches and thus severely affect the accuracy of joint space narrowing quantifications. Methods: In this work, we introduce a rotation invariant phase only correlation in joint space narrowing quantification for the joint angle correction. Further, we propose a confidence index to quantify the quantification reliability of phase only correlation based on phase dispersion in phase difference spectrum. Conclusion: In our clinical experiments, the proposed quantification method can effectively overcome and manage the mismatch due to the inconsistency of joint angles. Additionally, the confidence index shows a high consistency with the joint space narrowing progression examinations manually done by a trained radiologist and one radiological technologist.
文摘针对车载雷达多参数联合超分辨计算复杂度高、无法快速实现参数估计的问题,提出了基于频域波束降维的多参数联合超分辨算法。所提算法通过快速傅里叶变换(fast Fourier transform,FFT)将空时多参数域联合数据变换到频域,处理感兴趣区域的多维频域数据,完成空时波束空间降维和基于频域数据的多参数联合超分辨,实现目标信息的快速联合估计。推导了频域子空间正交性及频域波束降维超分辨算法理论。仿真研究了算法的分辨率和估计性能与信噪比的关系。仿真结果表明,所提算法的精度和分辨率远超传统FFT算法,相对于传统多重信号分类(multiple signal classification,MUSIC)算法,所提算法计算量大幅降低。
文摘瞄准制衡强敌“马赛克战”“决策中心战”等新技术驱动的作战概念及威胁挑战,聚焦未来跨域联合作战指挥控制(command and control,C2)全流程决策需求,遵循平行智能理论框架,提出了基于筹划-准备-执行-评估(planning-readiness-execution-assessment,PREA)环与观察-判断-决策-行动(observe-orient-decide-act,OODA)环的平行指挥控制与管理(command&control and management,C2M)新范式,以期实现智能机器辅助指挥员作战全流程的分层次、个性化决策支持,减轻指挥员认知负担、降低决策复杂度,实现机器对指挥员“人脑”的智能扩展与增强,为塑造全局决策优势提供牵引和支撑。
