@phdthesis{oai:kitakyu.repo.nii.ac.jp:00000624,
 author = {ミヤ　リズキニヤ},
 month = {2018-05-21},
 note = {This study aims to generalize color line to M-dimensional spectral line feature (M>3) and introduce methods for denoising and unmixing of hyperspectral images based on the spectral linearity.
For denoising, we propose a local spectral component decomposition method based on the spectral line. We first calculate the spectral line of an M-channel image, then using the line, we decompose the image into three components: a single M-channel image and two gray-scale images. By virtue of the decomposition, the noise is concentrated on the two images, thus the algorithm needs to denoise only two grayscale images, regardless of the number of channels. For unmixing, we propose an algorithm that exploits the low-rank local abundance by applying the unclear norm to the abundance matrix for local regions of spatial and abundance domains. In optimization problem, the local abundance regularizer is collaborated with the L2, 1 norm and the total variation.},
 school = {北九州市立大学},
 title = {Study on Denoising and Unmixing of Hyperspectral Images Exploiting Spectral Linearity},
 year = {}
}