MOPEX User's Guide - IRSA

MOPEX User’s Guide
The second outlier detection method carries out temporal filtering of the interpolated images. It is
performed by the Mosaic Outlier module. The input parameters used for this module are Bottom
Threshold, Top Threshold, and Min Pix Number.
Important: Using this module does not mean that MOPEX will automatically use the results
for outlier detection. In order to use the results from this module, you must check the Use
Outlier for RMask box in the Mosaic RMask module and set the RMask Fatal Mask Bit Pattern in
the Initial Setup module to use bit 1. If you are using the command line, include the
USE_OUTLIER_FOR_RMASK trigger in the namelist and set RMask_Fatal_BitPattern to use bit
1. Note that this is not the same as setting it to a value of 1. See §8.11: Fatal Mask Bit Patterns for
more information.
The input images are interpolated onto a common grid, such that the pixels in the interpolated
images are perfectly lined up and create a stack of pixels. Each spatial location, i.e. a pixel in the
common grid, is filtered in the time domain and outliers are found (see Figure 8.5).
If pixel value Ik for pixel k in the stack satisfies either of the following conditions:
Ik < M - Bottom Threshold *σ
Ik > M + Top Threshold *σ
pixel k is declared an outlier. M and σ are estimates of the mean and scatter of the pixel values in
the stack. If pixel uncertainty images are provided σ above is set to the greater of two values: the
scatter and the smallest pixel uncertainty in the stack. The scatter cannot be reliably estimated
with only a handful of samples. If the stack size is less than Min Pix Number, then σ is set to the
smallest pixel uncertainty in the stack.
Figure 8.5: Interpolated images (red) are stacked up and shown here in the side view. The
underlying grid is shown in black.
Basic Concepts in MOPEX 184
Out lier Det ect ion