Efficient Target Detection from Hyperspectral Images Based On Removal of Signal Independent and Signal Dependent Noise

In our proposed system the random noise present in hyper spectral image is removed by means of tensor based decomposition methods. The noises present in hyper spectral images are classified into two categories namely: signal independent noise and signal dependent noise. The noises present in the hyper spectral images have dependence on the noise variance of the signal. The input image is separated into seven different frequency bands and noise is added into some of those bands. The corresponding peak signal to noise ratio was calculated for each band based on mean square error value. In our project we are using GUI tool in MATLAB to enable user friendly approach in noise removal. The overall system comprises of three types of algorithm namely: parallel factor analysis (PARAFAC), hyper spectral noise estimation (HYNE) and multidimensional Wiener filter (MWF).The first one, named as the PARAFACSI–PARAFACSD method, uses a multi linear algebra model, PARAFAC decomposition, twice to remove SI and SD noise, respectively. The second one is a combination of the multiple-linear-regression-based approach termed as the HYNE method and PARAFAC decomposition, which is named as the HYNE-PARAFAC method. The last one combines the MWF method and PARAFAC decomposition and is named as the MWF-PARAFAC method. SI noise is removed from the original image by PARAFAC decomposition, HYNE method, or MWF method based on the property of SI Noise. SD components can be further reduced by PARAFAC decomposition due to its own statistical property.