Spatially Variant Dimensionality Reduction for the Visualization of Multi/Hyperspectral Images (original) (raw)

Enhanced Visualization of Hyperspectral Images

We present an enhanced visualization algorithm for hyperspectral images (HSIs). The visualization is based on the projection onto color matching functions of the human vision system. A contrast enhancement procedure is introduced by the fusion of the gradient information of the individual HSI bands. Both visualization and enhancement are combined into a multiresolution framework using wavelets. The HSI is transformed into a specific representation (HSI wavelet representation), in which the enhancement is performed at the level of the wavelet detail subbands, whereas the visualization is performed at the level of the low-resolution subbands. Specific objective quality measures are applied to demonstrate that the proposed procedure provides visualization results with a high contrast. Results are compared with state-of-the-art HSI visualization techniques and with the postprocessing enhancement.

Comparison and Evaluation of Dimensionality Reduction Techniques for Hyperspectral Data Analysis

The 2nd International Electronic Conference on Geosciences, 2019

Hyperspectral datasets provide explicit ground covers with hundreds of bands. Filtering contiguous hyperspectral datasets potentially discriminates surface features. Therefore, in this study, a number of spectral bands are minimized without losing original information through a process known as dimensionality reduction (DR). Redundant bands portray the fact that neighboring bands are highly correlated, sharing similar information. The benefits of utilizing dimensionality reduction include the ability to slacken the complexity of data during processing and transform original data to remove the correlation among bands. In this paper, two DR methods, principal component analysis (PCA) and minimum noise fraction (MNF), are applied to the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) dataset of Kalaburagi for discussion.