Hyperspectral imaging is one of the cutting edge technology in the remote sensing field and it has many successful applications. One of prominent advantages of hyperspectral imaging technology is it can acquire the continuous spectral information of materials while collecting the geometric information of ground objects. This characteristic of hyperspectral images (HSIs) has brought tremendous changes for the applications of the remote sensing technology. On one hand, it deepens the applications of the remote sensing technology, for example, by utilizing spectral features of materials, we can distinguish two materials with very small difference; on the other hand, it provides more means for the applications of remote sensing and make the quantitative analysis of objects become possible, for instance, the unmixing techniques of HSIs not only can determine the spectra of endmembers, but also can determine the fraction of each endmember in each pixel. The development of hyperspectral imaging technology makes the applications of remote sensing technology based on the images and spectra toward a more refined and accurate direction, it also puts forward higher requirements on image quality. The process of hyperspectral imaging is generally interfered by the acquisition environment, the sensor noise and other uncertain factors, thus the acquired raw data often contain a variety of noise. The noise will degrade the image and has a negative effect on information extraction. To reduce the noise of the HSIs acquired by spaceborne or airborne imaging spectroscopy and enhance the qualities of HSIs is the pre-requisite task for the processing of HSIs. Compared with the traditional wide-band remote sensing, the image qualities of imaging spectroscopy are more vulnerable to inferences from capturing environment, such as the atmospherical absorption and scattering. The noise sources of HSIs and their characteristics are also more complex. The HSI is a 3D tensor which includes two spatial dimensions and one spectral dimension, its characteristics are different from the traditional multi-spectral images. Thus, denoising approaches for the traditional images are not suitable to HSIs and we should investigate new denoising methods according to the characteristics of HSIs. Based on analyzing the factors influencing the imaging quality of spectral imaging, the noise sources and noise characteristics of HSIs, we propose several denoising schemes for HSIs in this thesis. Dur...
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