Hyperspectral image compression based on lapped transform and Tucker decomposition

• We design a hyperspectral image compression using lapped transform and Tucker decomposition.
• Each band of a hyperspectral image is decorrelated by a lapped transform.
• Transformed coefficients of various frequencies are rearranged in 3Dwavelet subband structures.
• 3D subbands are viewed as third-order tensors, decomposed by Tucker decomposition.
• The core tensor is encoded using a bit-plane coding algorithm into bit-streams.

In this paper, we present a hyperspectral image compression system based on the lapped transform and Tucker decomposition (LT-TD). In the proposed method, each band of a hyperspectral image is first decorrelated by a lapped transform. The transformed coefficients of different frequencies are rearranged into three-dimensional (3D) wavelet sub-band structures. The 3D sub-bands are viewed as third-order tensors. Then they are decomposed by Tucker decomposition into a core tensor and three factor matrices. The core tensor preserves most of the energy of the original tensor, and it is encoded using a bit-plane coding algorithm into bit-streams. Comparison experiments have been performed and provided, as well as an analysis regarding the contributing factors for the compression performance, such as the rank of the core tensor and quantization of the factor matrices.