Perceptual-based hyperspectral image fusion using multiresolution analysis

Terry A. Wilson; Steven K. Rogers; Lemuel R. Myers Jr.

doi:10.1117/12.213617

1 November 1995 Perceptual-based hyperspectral image fusion using multiresolution analysis

Terry A. Wilson, Steven K. Rogers, Lemuel R. Myers Jr.

Author Affiliations +

Optical Engineering, Vol. 34, Issue 11, (November 1995). https://doi.org/10.1117/12.213617

Abstract

A perceptual-based multiresolution image fusion technique is demonstrated using the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor data. The AVIRIS sensor, which simultaneously collects information in 224 spectral bands that range from 0.4 to 2.5 μm in approximately 10-nm increments, produces 224 images, each representing a single spectral band. The fusion algorithm consists of three stages. First, a Daubechies orthogonal wavelet basis set is used to perform a multiresolution decomposition of each spectral image. Next, the coefficients from each image are combined using a perceptual-based weighting. The weighting of each coefficient, from a given spectral band image, is determined by the spatial-frequency response (contrast sensitivity) of the human visual system. The spectral image with the higher saliency value, where saliency is based on a perceptual energy, will receive the larger weight. Finally, the fused coefficients are used for reconstruction to obtain the fused image. The image fusion algorithm is analyzed using test images with known image characteristics and image data from the AVIRIS hyperspectral sensor. By analyzing the signal-to-noise ratios and visual aesthetics of the fused images, contrast-sensitivity-based fusion is shown to provide excellent fusion results and to outperform previous fusion methods.

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Terry A. Wilson, Steven K. Rogers, and Lemuel R. Myers Jr. "Perceptual-based hyperspectral image fusion using multiresolution analysis," Optical Engineering 34(11), (1 November 1995). https://doi.org/10.1117/12.213617

Published: 1 November 1995

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