The spaceborne synthetic aperture radar(SAR)sparse flight 3-D imaging technology through multiple observations of the cross-track direction is designed to form the cross-track equivalent aperture,and achieve the third...The spaceborne synthetic aperture radar(SAR)sparse flight 3-D imaging technology through multiple observations of the cross-track direction is designed to form the cross-track equivalent aperture,and achieve the third dimensionality recognition.In this paper,combined with the actual triple star orbits,a sparse flight spaceborne SAR 3-D imaging method based on the sparse spectrum of interferometry and the principal component analysis(PCA)is presented.Firstly,interferometric processing is utilized to reach an effective sparse representation of radar images in the frequency domain.Secondly,as a method with simple principle and fast calculation,the PCA is introduced to extract the main features of the image spectrum according to its principal characteristics.Finally,the 3-D image can be obtained by inverse transformation of the reconstructed spectrum by the PCA.The simulation results of 4.84 km equivalent cross-track aperture and corresponding 1.78 m cross-track resolution verify the effective suppression of this method on high-frequency sidelobe noise introduced by sparse flight with a sparsity of 49%and random noise introduced by the receiver.Meanwhile,due to the influence of orbit distribution of the actual triple star orbits,the simulation results of the sparse flight with the 7-bit Barker code orbits are given as a comparison and reference to illuminate the significance of orbit distribution for this reconstruction results.This method has prospects for sparse flight 3-D imaging in high latitude areas for its short revisit period.展开更多
基于单样本的人脸识别是一项充满挑战性的任务。文中结合Similar Principal Component Analysis(SPCA)算法与Histograms of Oriented Gradients(HOG)算法,利用SPCA筛选出图像类的相似信息,用HOG算法对相似的信息块进行特征量化,使二者...基于单样本的人脸识别是一项充满挑战性的任务。文中结合Similar Principal Component Analysis(SPCA)算法与Histograms of Oriented Gradients(HOG)算法,利用SPCA筛选出图像类的相似信息,用HOG算法对相似的信息块进行特征量化,使二者优势互补。最后利用Pearson correlation(PC)进行相似性判别,在数据库Extended Yale B database上进行实验,结果表明,在光照变化的情况下,该算法对人脸正面图像的识别性能比传统算法好。展开更多
提出了一种结合KPCA(Kernel Principal Component Analysis)和稀疏表示的合成孔径雷达(Synthetic Aperture Rader,SAR)目标识别方法。该方法首先利用KPCA方法提取样本特征,然后在特征空间内构造稀疏表示模型,通过梯度投影法(Gradient Pr...提出了一种结合KPCA(Kernel Principal Component Analysis)和稀疏表示的合成孔径雷达(Synthetic Aperture Rader,SAR)目标识别方法。该方法首先利用KPCA方法提取样本特征,然后在特征空间内构造稀疏表示模型,通过梯度投影法(Gradient Projection for Sparse Reconstruction,GPSR)求得测试样本的稀疏系数,最后根据稀疏系数的能量特征实现分类识别。利用美国运动和静止目标获取与识别(Moving and Stationary Target Acquisition and Recognition,MSTAR)实测SAR数据进行实验,实验结果表明该方法在方位角未知的情况下平均识别率达到96.78%,能够明显地提高目标的识别结果,是一种有效的SAR目标识别方法。展开更多
基金This work was supported by the General Design Department,China Academy of Space Technology(10377).
文摘The spaceborne synthetic aperture radar(SAR)sparse flight 3-D imaging technology through multiple observations of the cross-track direction is designed to form the cross-track equivalent aperture,and achieve the third dimensionality recognition.In this paper,combined with the actual triple star orbits,a sparse flight spaceborne SAR 3-D imaging method based on the sparse spectrum of interferometry and the principal component analysis(PCA)is presented.Firstly,interferometric processing is utilized to reach an effective sparse representation of radar images in the frequency domain.Secondly,as a method with simple principle and fast calculation,the PCA is introduced to extract the main features of the image spectrum according to its principal characteristics.Finally,the 3-D image can be obtained by inverse transformation of the reconstructed spectrum by the PCA.The simulation results of 4.84 km equivalent cross-track aperture and corresponding 1.78 m cross-track resolution verify the effective suppression of this method on high-frequency sidelobe noise introduced by sparse flight with a sparsity of 49%and random noise introduced by the receiver.Meanwhile,due to the influence of orbit distribution of the actual triple star orbits,the simulation results of the sparse flight with the 7-bit Barker code orbits are given as a comparison and reference to illuminate the significance of orbit distribution for this reconstruction results.This method has prospects for sparse flight 3-D imaging in high latitude areas for its short revisit period.
文摘基于单样本的人脸识别是一项充满挑战性的任务。文中结合Similar Principal Component Analysis(SPCA)算法与Histograms of Oriented Gradients(HOG)算法,利用SPCA筛选出图像类的相似信息,用HOG算法对相似的信息块进行特征量化,使二者优势互补。最后利用Pearson correlation(PC)进行相似性判别,在数据库Extended Yale B database上进行实验,结果表明,在光照变化的情况下,该算法对人脸正面图像的识别性能比传统算法好。
文摘提出了一种结合KPCA(Kernel Principal Component Analysis)和稀疏表示的合成孔径雷达(Synthetic Aperture Rader,SAR)目标识别方法。该方法首先利用KPCA方法提取样本特征,然后在特征空间内构造稀疏表示模型,通过梯度投影法(Gradient Projection for Sparse Reconstruction,GPSR)求得测试样本的稀疏系数,最后根据稀疏系数的能量特征实现分类识别。利用美国运动和静止目标获取与识别(Moving and Stationary Target Acquisition and Recognition,MSTAR)实测SAR数据进行实验,实验结果表明该方法在方位角未知的情况下平均识别率达到96.78%,能够明显地提高目标的识别结果,是一种有效的SAR目标识别方法。
