Wavelet based sparse Principal Component Analysis for hyperspectral denoising

Principal Component Analysis (PCA) has widely been used in hyperspectral image analysis as a preprocessing step for further processing. Recently, sparse PCA methods have emerged as a powerful alternative. In this paper we propose a wavelet based sparse PCA method for hyperspectral image denoising. The proposed method is evaluated by using simulated and real data.