离散小波变换域非负张量分解的高光谱遥感图像压缩

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40 dB,与传统高光谱图像压缩算法比较,平均峰值信噪比提高了1.499 dB.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.

Hyper-spectral Remote Sensing Image Compression Based on Nonnegative Tensor Factorizations in Discrete Wavelet Domain

A hyper-spectral image compression algorithm based on nonnegative tensor factorizations is proposed in this paper. First, every band of hyper-spectral images is decomposed by 2D 5/3 discrete wavelet transform to reduce the space redundancy of hyper-spectral images. Then, the four DWT sub-bands of the each level DWT for all spectral coverage are used as four tensors. And each sub-band tensor is decomposed by the proposed improved Hierarchical Alternating Least Squares (HALS) algorithm to reduce the spectra redundancy and the residual space redundancy. The algorithm can also protect the spectral information. Finally, the factorizations matrix is encoded by an entropy coder. The experimental results show that the proposed compression algorithm has good compressive property. In the compression ration range from 32:1 to 4:1, the average peak signal to noise ratio of proposed compression algorithm is higher than 40 dB. Compared with traditional approaches, the proposed method could improve the average PSNR by 1.499 dB. The compression performance of hyper-spectral image is effectively improved and the spectral information is protected.