Band-Specific Shearlet-Based Hyperspectral Image Noise Reduction

Shearlet-Based Adaptive Noise Reduction in CT Images

The noise in reconstructed slices of X-ray Computed Tomography (CT) is of unknown distribution, non-stationary, oriented and difficult to distinguish from main structural information. This requires the development of special post-processing methods based on the local statistical evaluation of the noise component. This paper presents an adaptive method of reducing noise in CT images employing th...

Hyperspectral Image Mixed Noise Reduction Based on Improved K-svd Algorithm

We propose an algorithm for mixed noise reduction in Hyperspectral Imagery (HSI). The hyperspectral data cube is considered as a three order tensor. These tensors give a clear view about both spatial and spectral modes. The HSI provides ample spectral information to identify and distinguish spectrally unique materials, thus they are spectrally over determined. Tensor representation is three ord...

Band Selection of Hyperspectral-Image Based Weighted Indipendent Component Analysis

Huge amounts of data in hyperspectral images have been caused to represent approaches for the band selection of these images. In this paper, a new approach based on independent component analysis (ICA) is proposed. The idea of projection pursuit is used to order the bands on the basis of a non-gaussianity distribution. Applying a negentropy function to weight bands is a novel idea that leads to...

Firefly Based Band Selection for Hyperspectral Image Classification

Band reduction for hyperspectral imagery processing

Feature reduction denotes the group of techniques that reduce high dimensional data to a smaller set of components. In remote sensing feature reduction is a preprocessing step to many algorithms intended as a way to reduce the computational complexity and get a better data representation. Reduction can be done by either identifying bands from the original subset (selection), or by employing var...