Hyperspectral Image Processing by Jointly Filtering Wavelet Component Tensor

Denoising is an important preprocessing step for several applications in the hyperspectral imaging (HSI) domain, such as classification and target detection, to achieve good performances. Because the signal-dependent photonic noise has become as dominant as the signal-independent noise generated by the electronic circuitry in HSI data collected by new-generation hyperspectral sensors, the reduction of the additive signal-dependent photonic noise becomes the focus of the current research in this field. To reduce the optoelectronic noise from HSIs, a new method is developed in this paper. First, a prewhitening procedure is proposed to whiten noise in HSIs. Second, a multidimensional wavelet packet transform (MWPT) in tensor form is presented to find different component tensors of the HSI. Then, to jointly filter a component tensor in each mode, a multiway Wiener filter is introduced. Moreover, to determine the best transform level and basis of the MWPT, a risk function is proposed. The effectiveness of our method in denoising and classification is experimentally demonstrated on a real-world HSI acquired by an airborne sensor.