情報地質 第26巻 第2号 082-083頁 2015年
Geoinformatics, vol.26, no.2, pp.082-083, 2015

Simulation of Hyperspectral Imagery from Landsat Imagery for Detailed
Mineral Mapping
Nguyen Tien Hoang* and Katsuaki Koike**Graduate School of Engineering, Kyoto University, Katsura C1–2–215, Kyoto 615–8540, Japan.
E–mail: koike.katsuaki.5x@kyoto–u.ac.jp (K. Koike)Key words: Hyperion imagery, Multivariate analysis, BMA, Spectral reconstruction

1. Introduction
Minerals can be detected from remotely sensed imagery bycombining geologic and fracture mapping with the recognitionof hydrothermally altered rocks. Generation of mostepithermal vein deposits has been related to hydrothermal
alteration of the adjacent country rocks (Sabins, 1999).
Passive sensors were applied in geological applications,
ranging from a few spectral bands to more than 100
contiguous bands, covering visible to shortwave infrared
regions of the electromagnetic spectrum. Development ofmulti– and hyper–spectral geologic remote sensing isclassified into the Landsat era, ASTER era, and hyperspectralera (van der Meer et al., 2012). EO–1 Hyperion, a
representative hyperspectral sensor, has been applied to maphydrothermal alteration by covering the 0.4 – 2.5 µm range
with 242 bands at approximately 10 nm spectral resolution
and 30 m spatial resolution. Despite this spectral superiority,
its image scene is much narrower than Landsat image scene.
Based on that background, 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. The pseudo–hyperspectral
imagery can have the number of bands as same as thenumber of high–quality Hyperion bands, and the same swath
width as Landsat scene. The resultant pseudo–hyperspectral
imagery must contribute to detailed identification of minerals
than the traditional satellite imagery.

analysis because they suffer from noise and sensor artefacts(Farifteh et al., 2013). In this study, the Hyperion data werecorrected for outlier pixels, vertical strips, and smile effects
prior to atmospheric calibration. Both data were corrected forthe atmospheric effects using ENVI–FLAASH module, andHyperion scene was co–registered to Landsat scene.
Assuming that multivariate linear regression models canbe hold between each of Hyperion bands and Landsat bands,
Bayesian model averaging method (BMA) was applied toselect the best model from a set of possible models. This bestmodel is used to build pseudo–hyperspectral data which has
the same swath width as the Landsat scene. A multivariate
linear regression model is:
=+.

.

+.

+.

++.

+.

+
(1)

where Hij represents pixel value of Hyperion image at band iand location j; β0i is intercept at Hyperion band i; β1i, β2i, β3i, β4i,β5i, and β6i are unknown regression coefficients between
Landsat bands and Hyperion band i; L1j, L2j, L3j, L4j, L5j, andL6j represent pixel values at location j of Landsat band 1, 2, 3,4, 5, and 7, respectively; and εij is random error (residual) at
band i and location j.
BMA accounts for the model uncertainty inherent in thevariable selection problem by averaging over the best models
in the model class according to approximate posterior modelprobability. The posterior probability is derived by means ofBayes’ theorem (Culka, 2014).

2. Study area and data
For a case study, we selected an area with 6 km × 7.1 km
size in the southwestern Nevada volcanic field in the US,
which is located about 10 km northwest of the Gold Mountainand 30 km southwest of the Cuprite alteration zones. Becauseof an extremely arid climate condition, this region is barren
and sparsely vegetated land, which is suitable for remotesensing–based mineral mapping. Cuprite served as the testsite of many remote sensing instruments including airborne
and orbital visible, near–infrared, thermal–infrared, andhyperspectral sensors (Swayze et al., 2014).
We used two cloud–free images on 23 July 2001, Landsat 7
ETM+ and EO–1 Hyperion images. Both satellites have thesun–synchronous orbit at an altitude of 705 km and provide
Earth imagery at a 30 m spatial resolution.

3. Methodology
The Hyperion data should be corrected prior to any data

( | , , )=( | ) ( | )∫( | ) ( | )(2)where p(D|ω) is a formal probability model for some(unknown) value of ω, the probabilistic mechanism which hasgenerated the observed data D; p(ω|K) is the prior probability
distribution over the sample space Ω, describing the available(expert) knowledge K about the value of ω prior to the data
being observed; and p(ω|D, A, K) is the posterior probability
density. The best model shows the lowest BayesianInformation Criterion (BIC) and the highest posterior
probability.

4. Results and discussion
Based on the model selection results by BMA, Landsat
imagery was transformed into 155 bands of pseudo–
hyperspectral imagery. Most models have multiple R–

83

0.01

Number of cases0.03

0.04

0.05

0.30
0.25
0.20
0.15
0.10
0.10

0.15

0.20

0.25

0.30

0.35

Hyperion band 25Count20

0.02

0.88
0.90
0.92
0.94
0.96
Figure 3. Boxplot of RMSE (above) and Pearson's coefficient(below) between the pseudo– and original bands.
Band 25 of pseudo-hyperspectral imagery

squared values higher than 90%, which assures highaccuracy of the models. The lowest one is 76% (Fig. 1). Band 5of Landsat imagery appears the most frequently (152 times),
while Band 7 is less frequent (105 times) in the models.
Comparing the pair of images in Figure 2, there are no clear
differences between the pseudo– and original data. The
pattern, texture, shape, and border of objects in both images
look quite similar, but the tone of color is slightly brighter inthe pseudo–hyperspectral data. This difference may becaused by that we calculated the regression model for all thepixels of each band without considering classification ofsurface features. The transformation results would have beenmuch better if a land cover map or a geologic map was used tofind the best model of each feature at every bands.
Two boxplots of Root Mean Square Error (RMSE) and thecorrelation coefficients between each band of original
Hyperion data and the pseudo–hyperspectral data are shownin Figure 3. Most bands have Pearson's coefficients > 0.95,
and a small fraction have the coefficients < 0.93 like outliersin the data sets. Band 220 has the lowest correlation
coefficient of 0.87. In a similar manner, RMSE values aremostly smaller than 0.014, which is considerably low. Because
only RMSE of band 160 is 0.052, this band should be removed
from further processing.

25Figure 4. Scatter plot of pseudo– and original data of band 25.

2015101050Figure 4 shows a strong correlation and high linearity
between pseudo– and original band 25. Most pseudo–bands
have the same linearity as the band 25 with the original
bands. These observations suggest that the statisticalsuitability of PHISA.

00.75

0.80

0.85

0.90

0.95

Multiple R-squared

We have developed a transformation method of Landsat
imagery into pseudo–Hyperion imagery, PHISA. A total of155 pseudo–bands are simulated with high accuracy by themultivariate linear regression models between each ofHyperion bands and Landsat bands and the Bayesian model
averaging method. Strong correlations between each band ofHyperion data and the pseudo–hyperspectral data wereconfirmed. We are now in the process of mineral mapping
using the pseudo–Hyperion imagery, and improving PHISA
by searching the best model of each feature at every bands.

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0.25

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Figure 1. Histogram of multiple R–squared.

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References
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5. Conclusion

461000Figure 2. Visual comparison of Hyperion data (left) andpseudo– hyperspectral data (right): (a) and (b) are images ofband 25, and (c) and (d) are images of band 159.

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