Hybrid hyperspectral image compression technique for non-iterative factorized tensor decomposition and principal component analysis: application for NASA’s AVIRIS data

Abstract

Remote sensing data has shown tremendous potential for applications in various fields like land use mapping and detection, geologic mapping, water resource applications, wetland mapping, urban and regional planning, environment inventory, natural disaster assessment, archaeological applications, and others. Every day, thousands of gigabytes of memory are involved in capturing the hyperspectral remote sensing datasets. The compelling information present in these hyperspectral images (HSIs) is very minimal due to redundancy. Spatial and spectral correlations monopolize the acquired HSI data sets. Therefore, an algorithm that exploits these correlations and compresses the HSI tensors is proposed in this paper. First, the acquired HSI image (reflectance data) is subjected to the removal of geometric and radiometric errors. Second, spectral bands of interest affiliated to the underlying application are exclusively processed for principal component analysis (PCA). Results of this PCA are scrutinized to identify the absolute dependent components. Further, these components are exposed to a non-iterative factorized compression technique. As a result, HSI 3D tensors are disintegrated into 1D tensors. This tensor breakdown leads to a compression ratio as high as 3747:1 while the total encoding time observed is 332 s and RMSE is as low as 0.0017. Later, the original HSI is reconstructed back by the product of decomposed individual tensors and its PSNR is 53.03 dB. The proposed compression method targets the tucker decomposition-based HSI compression technique which is computationally complex and time consuming, and hence, a breakthrough is achieved with the technique introduced.