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Proceedings Paper

Development of Bayesian-based transformation method of Landsat imagery into pseudo-hyperspectral imagery
Author(s): Nguyen Tien Hoang; Katsuaki Koike
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Paper Abstract

It has been generally accepted that hyperspectral remote sensing is more effective and provides greater accuracy than multispectral remote sensing in many application fields. EO-1 Hyperion, a representative hyperspectral sensor, has much more spectral bands, while Landsat data has much wider image scene and longer continuous space-based record of Earth's land. This study aims to develop a new method, Pseudo-Hyperspectral Image Synthesis Algorithm (PHISA), to transform Landsat imagery into pseudo hyperspectral imagery using the correlation between Landsat and EO-1 Hyperion data. At first Hyperion scene was precisely pre-processed and co-registered to Landsat scene, and both data were corrected for atmospheric effects. Bayesian model averaging method (BMA) was applied to select the best model from a class of several possible models. Subsequently, this best model is utilized to calculate pseudo-hyperspectral data by R programming. Based on the selection results by BMA, we transform Landsat imagery into 155 bands of pseudo-hyperspectral imagery. Most models have multiple R-squared values higher than 90%, which assures high accuracy of the models. There are no significant differences visually between the pseudo- and original data. Most bands have Pearson's coefficients < 0.95, and only a small fraction has the coefficients < 0.93 like outliers in the data sets. In a similar manner, most Root Mean Square Error values are considerably low, smaller than 0.014. These observations strongly support that the proposed PHISA is valid for transforming Landsat data into pseudo-hyperspectral data from the outlook of statistics.

Paper Details

Date Published: 15 October 2015
PDF: 6 pages
Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430J (15 October 2015); doi: 10.1117/12.2194886
Show Author Affiliations
Nguyen Tien Hoang, Kyoto Univ. Graduate School of Engineering (Japan)
Hue Univ. (Viet Nam)
Katsuaki Koike, Kyoto Univ. Graduate School of Engineering (Japan)

Published in SPIE Proceedings Vol. 9643:
Image and Signal Processing for Remote Sensing XXI
Lorenzo Bruzzone, Editor(s)

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