A High Precision Parallel Principal Skewness Analysis Algorithm and Its Application to Remote Sensing Images

Principal Skewness Analysis (PSA), as a third-order extension of Principal Component Analysis (PCA), is often used for blind image separation, SAR image denoising, and hyperspectral feature extraction. However, the existing PSA algorithm can only obtain approximate solutions, which will affect the accuracy of subsequent image processing. In view of this problem, a high-precision Parallel Principal Skewness Analysis (PPSA) algorithm based on the existing PSA algorithm is proposed. The PPSA algorithm considers fully the data structure, and selects the eigenvectors of all slices of the co-skewness tensor as the initial value of the iteration, which can accurately obtain the actual solution. Simulation experiments and actual remote sensing image experiments verify the effectiveness and superiority of the PSA algorithm.