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Figure 3.14 – (Left) Image from Terra MODIS band 33 affected mostly with random
stripes (Right) Destriped result obtained after histogram matching and Haralick’s sloped
facet model filtering.
The application of Haralick’s model is not devoted to the correction of periodic stripes and
Rakwatin et al. suggest using the iterative facet filtering procedure only on specific pixels.
Detector-to-detector stripes are initially corrected via the histogram matching technique.
Lines acquired by noisy detectors are then visually detected and processed with the sloped
facet model. In pratice, we selected a facet window of size 3 × 3 pixels. As the number of
iterations of the weighted facet filtering procedure increases (3.25), the visual impact of
random stripes decreases. For the image from Terra MODIS band 33, 10 iterations were
required to achieve a cosmetic improvement (see figure 3.14).
3.7 Multiresolution approach
3.7.1 Limitations of fourier transform
The Fourier transform is a remarquable tool in signal processing. Shifting to the frequency
domain offers the possibility to extract information that would otherwise be unperceptible
in the spatial or temporal domain. Nevertheless, the fourier transform has a
major drawback. The shift in fourier domain is inevitably followed by a loss of temporal/spatial
information ; The frequency of a given event can only be known at the expense
of it occuring times. A compromise can be achieved with the short-term fourier transform
(STFT). It consists in limiting the computation of fourier transform to local portions of
the signal, using a fixed size sliding analysing window. The Heiseinberg principle then
highlights the limitations of the STFT ; For small sized windows, a good temporal localisation
is achieved with approximative frequency localisation. For increasing windows size,