该文提出一种基于非负张量分解的高光谱图像压缩算法。首先将高光谱图像的每个谱段进行2维离散5/3小波变换,消除高光谱图像的空间冗余。然后将所有谱段的每级小波变换的4个小波子带看作为4个张量。对每个小波子带张量采用改进HALS(Hi...该文提出一种基于非负张量分解的高光谱图像压缩算法。首先将高光谱图像的每个谱段进行2维离散5/3小波变换,消除高光谱图像的空间冗余。然后将所有谱段的每级小波变换的4个小波子带看作为4个张量。对每个小波子带张量采用改进HALS(Hierarchical Alternating Least Squares)算法进行非负分解,来消除光谱冗余和空间残余冗余,同时保护了光谱信息。最后,将分解的因子矩阵进行熵编码。实验结果表明,该文提出的压缩算法具有良好压缩性能,在压缩比32:1-4:1范围内,平均信噪比高于40dB,与传统高光谱图像压缩算法比较,平均峰值信噪比提高了1.499dB。有效地提高了高光谱图像压缩算法的压缩性能和保护了光谱信息。展开更多
该文提出了一种基于2维矢量接收阵列的双基地MIMO雷达系统多目标ADOD(Azimuth Direction Of Departure),ADOA(Azimuth Direction Of Arrival)和EDOA(Elevation Direction Of Arrival)联合估计算法。雷达发射端采用均匀标量线阵,接收端...该文提出了一种基于2维矢量接收阵列的双基地MIMO雷达系统多目标ADOD(Azimuth Direction Of Departure),ADOA(Azimuth Direction Of Arrival)和EDOA(Elevation Direction Of Arrival)联合估计算法。雷达发射端采用均匀标量线阵,接收端将常规矢量阵元的每个电磁偶极子相互分离构成2维接收阵列。算法通过张量因子分解获取各流形矩阵,并利用ESPRIT算法估计目标的ADOD。文中给出了接收阵列的一种特定阵元排列方式,并改进了矢量叉积法用于估计目标的2D-DOA。与传统方法相比,该文所用阵列结构可通过扩展接收阵列孔径提高雷达的角度估计性能,相互分离的偶极子弱化了传统矢量阵的天线互耦效应。相应算法避免了谱峰搜索,能够自动配对,仿真实验证明了算法的有效性。展开更多
高光谱图像(HSI)中的噪声去除是遥感技术中的一项基础而关键的任务,它对于图像的后续处理和分析至关重要。本项研究针对高光谱图像的去噪挑战,针对张量纤维秩约束优化与即插即用正则化的去噪技术对其中的不足进行了改进,即根据条带噪声...高光谱图像(HSI)中的噪声去除是遥感技术中的一项基础而关键的任务,它对于图像的后续处理和分析至关重要。本项研究针对高光谱图像的去噪挑战,针对张量纤维秩约束优化与即插即用正则化的去噪技术对其中的不足进行了改进,即根据条带噪声的组稀疏性质,通过L2-1范数对噪声中条带噪声组稀疏性质进行描述。有效提升了以往L1范数刻画条带噪声的去噪能力。最后通过应用乘子交替方向法(ADMM)来解决这一非凸优化问题。在多个遥感图像数据集上进行的实验验证了该方法在峰值信噪比(PSNR)和结构相似度(SSIM)等评价标准上的优越性,证明了其在处理复杂噪声条件下的高效性和广泛的应用前景。The noise removal in hyperspectral images (HSI) is a fundamental and crucial task in remote sensing technology, which is crucial for the subsequent processing and analysis of images. This study addresses the denoising challenge of hyperspectral images by improving the denoising techniques of tensor fiber rank constrained optimization and plug and play regularization. Based on the sparsity of band noise, the L2-1 norm is used to describe the band noise in the noise. It has improved the denoising ability of previous L1 norm characterization of stripe noise. Finally, by applying the Multiplier Alternating Directions Method (ADMM) to solve this non convex optimization problem, this method achieved a significant improvement in computational efficiency. Experiments conducted on multiple remote sensing image datasets have verified the superiority of this method in evaluation criteria such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), demonstrating its efficiency and broad application prospects in dealing with complex noise conditions.展开更多
文摘该文提出一种基于非负张量分解的高光谱图像压缩算法。首先将高光谱图像的每个谱段进行2维离散5/3小波变换,消除高光谱图像的空间冗余。然后将所有谱段的每级小波变换的4个小波子带看作为4个张量。对每个小波子带张量采用改进HALS(Hierarchical Alternating Least Squares)算法进行非负分解,来消除光谱冗余和空间残余冗余,同时保护了光谱信息。最后,将分解的因子矩阵进行熵编码。实验结果表明,该文提出的压缩算法具有良好压缩性能,在压缩比32:1-4:1范围内,平均信噪比高于40dB,与传统高光谱图像压缩算法比较,平均峰值信噪比提高了1.499dB。有效地提高了高光谱图像压缩算法的压缩性能和保护了光谱信息。
文摘该文提出了一种基于2维矢量接收阵列的双基地MIMO雷达系统多目标ADOD(Azimuth Direction Of Departure),ADOA(Azimuth Direction Of Arrival)和EDOA(Elevation Direction Of Arrival)联合估计算法。雷达发射端采用均匀标量线阵,接收端将常规矢量阵元的每个电磁偶极子相互分离构成2维接收阵列。算法通过张量因子分解获取各流形矩阵,并利用ESPRIT算法估计目标的ADOD。文中给出了接收阵列的一种特定阵元排列方式,并改进了矢量叉积法用于估计目标的2D-DOA。与传统方法相比,该文所用阵列结构可通过扩展接收阵列孔径提高雷达的角度估计性能,相互分离的偶极子弱化了传统矢量阵的天线互耦效应。相应算法避免了谱峰搜索,能够自动配对,仿真实验证明了算法的有效性。
文摘高光谱图像(HSI)中的噪声去除是遥感技术中的一项基础而关键的任务,它对于图像的后续处理和分析至关重要。本项研究针对高光谱图像的去噪挑战,针对张量纤维秩约束优化与即插即用正则化的去噪技术对其中的不足进行了改进,即根据条带噪声的组稀疏性质,通过L2-1范数对噪声中条带噪声组稀疏性质进行描述。有效提升了以往L1范数刻画条带噪声的去噪能力。最后通过应用乘子交替方向法(ADMM)来解决这一非凸优化问题。在多个遥感图像数据集上进行的实验验证了该方法在峰值信噪比(PSNR)和结构相似度(SSIM)等评价标准上的优越性,证明了其在处理复杂噪声条件下的高效性和广泛的应用前景。The noise removal in hyperspectral images (HSI) is a fundamental and crucial task in remote sensing technology, which is crucial for the subsequent processing and analysis of images. This study addresses the denoising challenge of hyperspectral images by improving the denoising techniques of tensor fiber rank constrained optimization and plug and play regularization. Based on the sparsity of band noise, the L2-1 norm is used to describe the band noise in the noise. It has improved the denoising ability of previous L1 norm characterization of stripe noise. Finally, by applying the Multiplier Alternating Directions Method (ADMM) to solve this non convex optimization problem, this method achieved a significant improvement in computational efficiency. Experiments conducted on multiple remote sensing image datasets have verified the superiority of this method in evaluation criteria such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), demonstrating its efficiency and broad application prospects in dealing with complex noise conditions.