Tomographic synthetic aperture radar(TomoSAR)imaging exploits the antenna array measurements taken at different elevation aperture to recover the reflectivity function along the elevation direction.In these years,for ...Tomographic synthetic aperture radar(TomoSAR)imaging exploits the antenna array measurements taken at different elevation aperture to recover the reflectivity function along the elevation direction.In these years,for the sparse elevation distribution,compressive sensing(CS)is a developed favorable technique for the high-resolution elevation reconstruction in TomoSAR by solving an L_(1) regularization problem.However,because the elevation distribution in the forested area is nonsparse,if we want to use CS in the recovery,some basis,such as wavelet,should be exploited in the sparse L_(1/2) representation of the elevation reflectivity function.This paper presents a novel wavelet-based L_(2) regularization CS-TomoSAR imaging method of the forested area.In the proposed method,we first construct a wavelet basis,which can sparsely represent the elevation reflectivity function of the forested area,and then reconstruct the elevation distribution by using the L_(1/2) regularization technique.Compared to the wavelet-based L_(1) regularization TomoSAR imaging,the proposed method can improve the elevation recovered quality efficiently.展开更多
The paramount importance and multi-purpose applications of underlying topography over forest areas have gained widespread recognition over recent decades, bringing about a variety of experimental studies on accurate u...The paramount importance and multi-purpose applications of underlying topography over forest areas have gained widespread recognition over recent decades, bringing about a variety of experimental studies on accurate underlying topography mapping. The highly spatial and temporal dynamics of forest scenarios makes traditional measuring techniques difficult to construct the precise underlying topography surface. Microwave remote sensing has been demonstrated as a promising technique to retrieve the underlying topography over large areas within a limited period, including synthetic aperture radar interferometry(InSAR), polarimetric InSAR(PolInSAR) and tomographic SAR(TomoSAR). In this paper, firstly, the main principle of digital elevation model(DEM) generation by InSAR and SAR data acquisition over forest area are introduced. Following that, several methods of underlying topography extraction based on InSAR, PolInSAR, and TomoSAR are introduced and analyzed, as well as their applications and performance are discussed afterwards. Finally, four aspects of challenge are highlighted, including SAR data acquisition, error compensation and correction, scattering model reconstruction and solution strategy of multi-source data, which needs to be further addressed for robust underlying topography estimation.展开更多
合成孔径雷达(Sythetic Aperture Radar,SAR)层析成像(TomoSAR)是一种多基线干涉测量技术,可沿垂直于视线(Perpendicular to the Line-Of-Sight,PLOS)方向估计功率谱图(Power Spectrum Pattern,PSP)即后向散射系数,从而实现三维成像。...合成孔径雷达(Sythetic Aperture Radar,SAR)层析成像(TomoSAR)是一种多基线干涉测量技术,可沿垂直于视线(Perpendicular to the Line-Of-Sight,PLOS)方向估计功率谱图(Power Spectrum Pattern,PSP)即后向散射系数,从而实现三维成像。本文提出一种改进的波束形成优化算法,在双约束鲁棒Capon波束形成算法(Doubly Constrained Robust Capon Beamforming,DCRCB)的基础上,结合L1范数的约束函数,构建交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)的代价函数,将DCRCB恢复的后向散射系数进行进一步稀疏优化,实现层析SAR的三维成像。ADMM算法以增广拉格朗日算法为基础,将较为复杂的全局求解问题转换为两个或多个更易求解的简单局部子问题。ADMM算法在迭代中,各子问题可分别完成稀疏重构和降噪运算,被分离的局部子问题代数式都较为简单,均能较容易地求出确定的解,且不必对其进行收敛运算与约束操作。因此,ADMM算法具有重建精度高的优势。本文采用2021年中国科学院空天信息创新研究院发布的山西运城地区的8通道机载阵列干涉SAR数据进行了实验验证,实验结果验证了算法的有效性。展开更多
Recently a new paradigm is emerging in synthetic aperture radar(SAR)three-dimensional(3D)imaging technology where the imaging performance is enhanced by exploiting SAR visual semantics.Here by“SAR visual semantics”,...Recently a new paradigm is emerging in synthetic aperture radar(SAR)three-dimensional(3D)imaging technology where the imaging performance is enhanced by exploiting SAR visual semantics.Here by“SAR visual semantics”,we mean primarily the scene conceptual structural information extracted directly from SAR images.Under this paradigm,a paramount open problem lies in what and how the SAR visual semantics could be extracted and used at different levels associated with different structural information.This work is a tentative attempt to tackle the above what-and-how problem,and it mainly consists of the following two parts.The first part is a sketchy description of how three-level(low,middle,and high)SAR visual semantics could be extracted and used in SAR Tomography(TomoSAR),including an extension of SAR visual semantics analysis(e.g.,facades and roofs)to sparse 3D points initially recovered via traditional TomoSAR methods.The second part is a case study on two open source TomoSAR datasets to illustrate and validate the effectiveness and efficiency of SAR visual semantics exploitation in TomoSAR for box-like 3D building modeling.Due to the space limit,only main steps of the involved methods are reported,and we hope,such neglects of technical details will not severely compromise the underlying key concepts and ideas.展开更多
基金This work was supported by the Fundamental Research Funds for the Central Universities(NE2020004)the National Natural Science Foundation of China(61901213)+3 种基金the Natural Science Foundation of Jiangsu Province(BK20190397)the Aeronautical Science Foundation of China(201920052001)the Young Science and Technology Talent Support Project of Jiangsu Science and Technology Associationthe Foundation of Graduate Innovation Center in Nanjing University of Aeronautics and Astronautics(kfjj20200419).
文摘Tomographic synthetic aperture radar(TomoSAR)imaging exploits the antenna array measurements taken at different elevation aperture to recover the reflectivity function along the elevation direction.In these years,for the sparse elevation distribution,compressive sensing(CS)is a developed favorable technique for the high-resolution elevation reconstruction in TomoSAR by solving an L_(1) regularization problem.However,because the elevation distribution in the forested area is nonsparse,if we want to use CS in the recovery,some basis,such as wavelet,should be exploited in the sparse L_(1/2) representation of the elevation reflectivity function.This paper presents a novel wavelet-based L_(2) regularization CS-TomoSAR imaging method of the forested area.In the proposed method,we first construct a wavelet basis,which can sparsely represent the elevation reflectivity function of the forested area,and then reconstruct the elevation distribution by using the L_(1/2) regularization technique.Compared to the wavelet-based L_(1) regularization TomoSAR imaging,the proposed method can improve the elevation recovered quality efficiently.
基金Projects(41820104005,41531068,41842059,41904004)supported by the National Natural Science Foundation of China。
文摘The paramount importance and multi-purpose applications of underlying topography over forest areas have gained widespread recognition over recent decades, bringing about a variety of experimental studies on accurate underlying topography mapping. The highly spatial and temporal dynamics of forest scenarios makes traditional measuring techniques difficult to construct the precise underlying topography surface. Microwave remote sensing has been demonstrated as a promising technique to retrieve the underlying topography over large areas within a limited period, including synthetic aperture radar interferometry(InSAR), polarimetric InSAR(PolInSAR) and tomographic SAR(TomoSAR). In this paper, firstly, the main principle of digital elevation model(DEM) generation by InSAR and SAR data acquisition over forest area are introduced. Following that, several methods of underlying topography extraction based on InSAR, PolInSAR, and TomoSAR are introduced and analyzed, as well as their applications and performance are discussed afterwards. Finally, four aspects of challenge are highlighted, including SAR data acquisition, error compensation and correction, scattering model reconstruction and solution strategy of multi-source data, which needs to be further addressed for robust underlying topography estimation.
文摘合成孔径雷达(Sythetic Aperture Radar,SAR)层析成像(TomoSAR)是一种多基线干涉测量技术,可沿垂直于视线(Perpendicular to the Line-Of-Sight,PLOS)方向估计功率谱图(Power Spectrum Pattern,PSP)即后向散射系数,从而实现三维成像。本文提出一种改进的波束形成优化算法,在双约束鲁棒Capon波束形成算法(Doubly Constrained Robust Capon Beamforming,DCRCB)的基础上,结合L1范数的约束函数,构建交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)的代价函数,将DCRCB恢复的后向散射系数进行进一步稀疏优化,实现层析SAR的三维成像。ADMM算法以增广拉格朗日算法为基础,将较为复杂的全局求解问题转换为两个或多个更易求解的简单局部子问题。ADMM算法在迭代中,各子问题可分别完成稀疏重构和降噪运算,被分离的局部子问题代数式都较为简单,均能较容易地求出确定的解,且不必对其进行收敛运算与约束操作。因此,ADMM算法具有重建精度高的优势。本文采用2021年中国科学院空天信息创新研究院发布的山西运城地区的8通道机载阵列干涉SAR数据进行了实验验证,实验结果验证了算法的有效性。
基金supported by the National Natural Science Foundation of China(61991423,62376269 and 62472464)the Key Scientific and Technological Project of Henan Province(232102321068)
文摘Recently a new paradigm is emerging in synthetic aperture radar(SAR)three-dimensional(3D)imaging technology where the imaging performance is enhanced by exploiting SAR visual semantics.Here by“SAR visual semantics”,we mean primarily the scene conceptual structural information extracted directly from SAR images.Under this paradigm,a paramount open problem lies in what and how the SAR visual semantics could be extracted and used at different levels associated with different structural information.This work is a tentative attempt to tackle the above what-and-how problem,and it mainly consists of the following two parts.The first part is a sketchy description of how three-level(low,middle,and high)SAR visual semantics could be extracted and used in SAR Tomography(TomoSAR),including an extension of SAR visual semantics analysis(e.g.,facades and roofs)to sparse 3D points initially recovered via traditional TomoSAR methods.The second part is a case study on two open source TomoSAR datasets to illustrate and validate the effectiveness and efficiency of SAR visual semantics exploitation in TomoSAR for box-like 3D building modeling.Due to the space limit,only main steps of the involved methods are reported,and we hope,such neglects of technical details will not severely compromise the underlying key concepts and ideas.
