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114 5. Application : Restoration of Aqua MODIS Band 6
missing data over vegetation, clouds, desert or oceanic surfaces require further refinement
to distinguish surface cover types. In addition, it is assumed that the polynomial relation
established on Terra data is transposable to Aqua. This assumption is all the more sensitive
to unknown calibration differences between Terra and Aqua MODIS, and the striping
noise described in the previous chapters. Furthermore, the analytical relation is derived
without prior pre-processing of bands 7 and 6 for stripe noise removal.
5.2.2 Local interpolation
More recently, Rakwatin2009 proposed a restoration procedure for Aqua MODIS band
6 that consists in three step :1) the determination of non functioning detectors ; 2) the
correction of periodic stripes for functioning detectors using histogram matching ; 3) the
estimation of missing pixels via local cubic polynomial regression between band 6 and band
7. Similarly to the approach proposed in [L. L. Wang and Nianzeng], the high correlation
between band 6 and 7 reflectances is quantified using polynomial regression. However, the
fitting is computed locally to account for land cover types. The restoring algorithm can
be summarized with the following :
1) For a dead pixel x in band 6, a initial rectangular window of size 15 × 3 is centered
at x. The minimum and maximum values of the window in band 7, respectively ρ min
7
and ρ max
7 and their location x min , x max are determined.
2) If ρ min
7 ≤ ρ 7 (x) ≤ ρ max
7 , a local cubic polynomial function is calculated from the
values ρ 7 (x min ), ρ 7 (x max ), ρ 6 (x min ) and ρ 6 (x max ) and used to estimated the values of
ρ 6 (x)
3) If ρ 7 (x) < ρ min
7 , ρ 7 (x) > ρ max
7 or if pixels x min or x max in band 6 are also dead,
the size of the analizing window is increased until these criteria are met.
4) Step 1, 2 and 3 are repeated for every dead pixel of band 6
This local cubic interpolation procedure is illustrateed in figure 5.3.
5.3 Proposed approach
We propose here a simple methodology to estimate the value of Aqua MODIS band 6
missing pixels. The approach is based on a concept widely used in the field of hyperspectral
image classification, spectral similarity. Data collected from MODIS can be perceived as a
cube, where the third dimension represents the signal’s wavelenght and can also be used to
extract useful information. In hyperspectral remote sensing, the determination of surface
composition requires the analysis of its reflectance spectrum and comparison with known
field spectra via spectral matching techniques [Kruse et al.]. Although conditionned by the
number of available spectral bands, this reasoning also applies to multispectral imagery.