【摘要】 将变换矩阵分解为三角可逆矩阵(TERM)实现的整数Karhunen-Loève变换(IKLT),具有结构简单、完全可逆和同址运算的优点.将整数KLT和整数小波结合(IWT),提出了一种基于可逆整数变换的去相关方法:将KLT用于去除谱间冗余,并在对KLT的变换矩阵进行TERM分解的过程中,提出基于全局最大值选择主元的优化分解方法,保证了IKLT的准确度,同时明显降低了计算量;空间维的去相关变换采用基于提升结构的整数小波变换,同样保证了变换的完全可逆.采用不同编码策略,对不同场景的高光谱图像数据压缩的实验结果表明,基于整数混合变换的去相关方法能明显提高无损压缩比.更多还原

【Abstract】 Hyperspectral imagery has very high spectral and spatial correlations.Since spectral information loss decreases the value of hyperspectral imagery for remote sensing applications,it is preferred to use lossless compression,although lossy compression can increase compression ratio.The Karhunen-Loève Transform is theoretically the optimal transform to decorrelate hyperspectral data.However,since its transformed signal is real number,KLT is hardly applied in the field of lossless compression.The integer KLT (IKLT) based on triangular elementary reversible matrices(TERM) factorization of the transform matirx is perfectly reversible,and can be computed in place.A lossless decorrelating algorithm for hyperspectral imagery compression combining the integer KLT and the integer wavelet transform (IWT) is proposed.A complete-maximum pivoting is used to constructed integer approximation of the KLT and leads to only limited error and more computational efficiency.In addition,given its promising performance in still image compresssion,an integer wavelet transform is implemented by the lifting scheme and adopted as spatial decorrelating transform,which is also inversible.The experimental results with different coding schemes and hyperspectral imagery from different scenes show that our decorrelating method can significantly enhance comprssion ratio.更多还原