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Principal Component Analysis (PCA) has widely been used in hyperspectral image analysis as a preprocessing step for further processing. Recently, sparse PCA methods have emerged as a powerful alternative. In this paper we propose a wavelet based sparse PCA method for hyperspectral image denoising. The proposed method is evaluated by using simulated and real data.
In this paper, a new denoising method for hyperspectral images using First Order Roughness Penalty (FORP) is proposed. The proposed algorithm is applied in the wavelet domain to exploit the multiresolution analysis property of wavelets and thus improving the denoising results. Stein's Unbiased Risk Estimator (SURE) is used to choose the tuning parameters automatically. The experimental results show...
In this work, multiscale local covariance matrices are proposed in the feature extraction step of unsupervised segmentation of the hyperspectral images. Producing groundtruth information for hyperspectral images is very expensive and time consuming process. For this reason, segmentation without label information brings an important advantage for easier analysis of the hyperspectral images. Proposed...
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