Hyperspectral Image Compression Using Hybrid Transform With Embedded Zero-Tree Wavelet And Set Partitioning In Hierarchical Tree

Hyperspectral images are three-dimensional representations comprising spatial and spectral dimensions. Hence, these images contain a huge volume of data, and compression is required for their efficient transmission, storage, and processing. We propose a hyperspectral image compression technique using a hybrid transform. Specifically, the integer Karhunen–Loève transform with clustering and tiling is applied to the spectral dimension to decorrelate the corresponding data. Then, the 2D integer discrete wavelet transform is applied over the spatial dimension to decorrelate the spatial data. In addition, the decorrelated wavelet coefficients are applied in the embedded zero-tree wavelet transform. Alternatively, the data are processed by an algorithm containing set partitioning in hierarchical tree. The wavelet transform and partitioning on the hybrid transform over hyperspectral images retrieve high peak signal-to-noise ratio, low number of bits per pixel per band, and fast computation time compared to similar approaches, with the partitioning showing the best results. Index Terms : Hyperspectral image; embedded zero-tree wavelet (EZW); set partitioning in hierarchical tree (SPIHT); integer Karhunen–Loève transform; integer discrete wavelet transform. ————————————————————