Denoising Hyperspectral Images Using Multijoint Method of Filter Estimation

Denoising the hyperspectral images (HSIs) which include both signal-dependent (SD) and signal- independent (SI) noise from signal. The signal dependent noise such as electron noise due to the electrical fluctuation in the current. The photon noise occurred during calibration process. The noise removed by using various methods. To denoise HSIs distorted by both signal-dependent (SD) and signal-independent (SI) noise, some hybrid methods, which reduce noise by two steps. The first one, named as the PARAFAC SI -PARAFAC SD method. The second one is the HYperspectral Noise Estimation (HYNE) method and PARAFAC decomposition, which is named as the HYNE-PARAFAC method. The last one combines the Wiener filter (MWF) method and PARAFAC decomposition and is named as the MWF-PARAFAC method. However, conventional hyperspectral imaging suffers from limited light in individual bands which introduces noise into the imaging process. In this paper, we present a simple but effective denoising method that exploits the spectral domain Index Terms: Denoising, hyperspectral image (HSI), PARAFAC, signal-dependent (SD) noise, signal- independent(SI), Multidimensional Wiener Filter(MWF). I. Introduction This document is based on Hyperspectral image which consist of hundreds of bands in the spectral. The several bands in the image called tensor. The noise in HSIs can be classified into two method which include random noise and photon noise. The random noise comes from signal dependent and signal independent noise. Military and civilian applications involve the detection of an object or activity such as a military vehicle or vehicle tracks. Hyperspectral imaging sensors provide image data containing both spatial and spectral information, and this information can be used to address such detection tasks. Hyperspectral imaging sensors measure the radiance of the materials within each pixel area at a very large number of contiguous spectral wavelength bands. In a passive remote sensing system the primary source of illumination is the sun. The distribution of the sun's emitted energy, as a function of wavelength throughout the electromagnetic