Lossless Compression of Hyperspectral Imagery with Reversible Integer Transform
基于可逆整数变换的高光谱图像无损压缩

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.