Hyperspectral image compression based on DWT and TD with ALS method

Compression of Hyper Spectral Image (HSI) is an important issue in remote sensing applications due to its huge data size. An efficient technique for HSI compression is proposed based on Discrete Wavelet Transform (DWT) and Tucker Decomposition (TD) with Adaptive Least Squares (ALS) method. This technique exploits both the spatial and spectral information in the images. ALS method is used to compute the TD which is applied on the DWT coefficients of HSI spectral bands. DWT is used to segment the HSIs into various sub-images, while TD is used to conserve the energy of the sub-images. Run Length Encoding (RLE) performs quantization of the component matrices and encoding of core tensors. The experiments are conducted with HSI compression based on DWT, TD with ALS method and HSI compression methods based on lossless JPEG (JPEG-LS), JPEG2000, Set Partitioning Embedded Block (SPECK), Object Based (OB)-SPEC and 3D-SPECK and the results of our work are found to be good in terms of Compression Ratio (CR) and Peak Signal-to-Noise Ratio (PSNR).