Development of Bayesian-based transformation method of Landsat imagery into pseudo-hyperspectral imagery

Nguyen Tien Hoang; Katsuaki Koike

doi:10.1117/12.2194886

15 October 2015 Development of Bayesian-based transformation method of Landsat imagery into pseudo-hyperspectral imagery

Nguyen Tien Hoang, Katsuaki Koike

Author Affiliations +

Proceedings Volume 9643, Image and Signal Processing for Remote Sensing XXI; 96430J (2015) https://doi.org/10.1117/12.2194886
Event: SPIE Remote Sensing, 2015, Toulouse, France

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.

Citation Download Citation

Nguyen Tien Hoang and Katsuaki Koike "Development of Bayesian-based transformation method of Landsat imagery into pseudo-hyperspectral imagery", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430J (15 October 2015); https://doi.org/10.1117/12.2194886

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