kaolinite--smectite (HyMAP channels 6, 81, 108 and 119). We
also defined diagnostic bands following the same criteria for a
group of Fe minerals such as goethite, hematite and jarosite
(HyMAP channels 4, 32, 62 and 103) and montmorillonite
(HyMAP channels 4, 25, 81 and 94).
(a)
Figure 2. Colour composite of abundance for minerals (Alu-
cyan, Illi-yelow, Kaol-purpel, Kaol+Sme-purpel2, Mont-
maroon) from HyMAP image (a), from MASTER image (b),
and thermal index THI (red-violet) between 10.16 um and
12.21 um from MASTER image (c).
Differents PCAs for each mineral in the image MASTER case
have been carried out using the following channels: MASTER
channels 4, 8, 21 and 24 for alunite; MASTER channels 4, 8, 21
and 22 for illite; MASTER channels 4, 13 22 and 24 for
kaolinite.
The results obtained for HyMap and MASTER have been
classified by K-Means algorithm. We calculated confusion
matrices using field sampling considered as true value. An
overlap accuracy of 82.5696 and Kappa coefficient of 0.75 have
been obtained for HyMAP image. An overlap of accuracy of
75.11% and Kappa coefficient of 0.69 have been obtained in the
MASTER case.
The results are strongly influenced by vegetation cover, which
acts as an input error of reflectance in the computing of new
variables or Principal Components (PCs). Nevertheless, we
observe the scarce presence of materials of clay-phyllic
alteration, contrasting with the abundance of iron oxide
components.
4.2 Spectral anomaly detection
A RX algorithm (Reed and Xaoli, 1996), widely accepted as a
standard spectral anomaly detection, has been applied to all set
of data.
Anomalies obtained for RX have been verified by those
computed using a method based on projection pursuit (Malpica
et al., 2008). The computation in both methods has been carried
out separately for spectral ranges of reflective channels and
emissive. MASTER thermal channels fused with aerial
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
photography at 2.5 m have been used to calculate thermal
anomalies with the intention on improve the classes separability
maintaining the raw spectral information acquired (Rejas ef al,
2007). Eight new variables were obtained from MASTER
channels 42 to 49. Variable number six was spatially projected
and we realized that is was related to high humidity values, and
it fitted a wide and well delimited area Northwest from
Turrialba.
We carried out pattern recognition analysis. For MASTER and
ASTER images we calculated a thermal index (Rejas ef al,
2009), profiting from the separability between covers in the
emissive spectrum of both sensors. We generated image
convolutions using a median filter, that were used afterwards to
make a ratio between 10.16 um y 12.21 um wavelengths,
weighted corrected by ratio between each channel gain. We
established thresholds on the resultant variable for highlighting
detected pixels as possible anomalies.
conv (5 els )- conv un )l 3)
THI. =
0.24 [conv C = conv (Fe J
where conv — image convolution median filter.
L m radiance at sensor for wavelength.
4.3 Results and Discusion
The relationship between spectral anomalies and hydrothermal
alterations, obtained in previous paragraphs, has been studied.
In order to do that, it has been linearly adjusted, a sample space
of 35 pairs of points, placing spectral anomalies in the Y axis
and altered minerals in the X axis. All regressions have been
calculated at a confidence level of 9596, removing in each
adjustment, the sample values that showed unusual residues and
which correspond mainly with clouds and shadows. The results
obtained are summarized in Tables 1 and 2.
HyMAP RX all chs 7 1101,61 * 9,86744*HA Alunite
600 -
Bu . R^ 2072
E = t
o Eo =
$40- ^.
© [2 o" a
© 300 - .
a. k s m
< L = ><a
= 200 . E.
Fa
T E ey EM
L un 5 o "ua
100 H s
0 " L 1 1 1 L 1 rd i i Lt; =
-190 -90 10 110
AH Alunite
Figure 3. Example of relationship between spectral anomalies
RX and Alunite, from HyMAP images.
2
Models HyMAP P-value |Correlation |R*
RX = 1101,61 + 9,86744*Alunite |0.0001 0.850365 72.31%
RX = -1528,05 - 1,92404*IIlite 0.0379 |-0.554625 |30.76%
RX = -611,046 + 1,03826*Kaolinite 10.0515 |0.511091 26.12%
RX = 739,318 - 7,59725*Kaol+Sme |0.0182 |-0.599314 35.92%
RX = 834,65 + 3,50578*Fe minerals |0.0153 |0.611924 37.45%
RX- -796,88 - 1,47957*Montmoroll | 0.0851 |-0.45922 21.09%
Table 1.
Comparison of relationships between spectral
anomalies and hydrothermal alteration, from HyMAP images.
