MOPEX Online Manual
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Modules: Mosaic Box Outlier
Namelist Trigger: run_mosaic_box_outlier
Output Directory Keyword: BOX_OUTLIER_DIR
Default Output Directory: <output_dir>/BoxOutlier
Depends on: Mosaic Interpolate
Important Notes: 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 include the USE_BOX_OUTLIER_FOR_RMASK trigger in the namelist and set the RMask_Fatal_BitPattern to use bit 3. Note that this is not the same as setting it to a value of 3. See Appendix 2: Fatal Bit Patterns for more information.
Output Directory Keyword: BOX_OUTLIER_DIR
Default Output Directory: <output_dir>/BoxOutlier
Depends on: Mosaic Interpolate
Important Notes: 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 include the USE_BOX_OUTLIER_FOR_RMASK trigger in the namelist and set the RMask_Fatal_BitPattern to use bit 3. Note that this is not the same as setting it to a value of 3. See Appendix 2: Fatal Bit Patterns for more information.
PURPOSE
-
This module uses the Box
Outlier rejection method to flag bad pixels. The method is
designed to use both the temporal and spatial information like the
dual outlier, but it uses the statistical analysis of the kind used by
the temporal outlier. In the identification of temporal outliers,
neighboring pixels are utilized to ensure the correct
classification.
PARAMETER BLOCK
-
&MOSAICOUTLIERIN
BOX_X = 3,
BOX_Y = 3,
BOX_MEDIAN_BIAS = 1,
TILE_XSIZ = 500,
TILE_YSIZ = 500,
&END
INPUTS
Box_X(Y): (int) the X (Y) size of the box of neighboring pixels used for outlier detections.
BOX_MEDIAN_BIAS: : (float) The computation of the mean and sigma for each pixel stack is done using a biased median. If there are N pixels in the stack, then the biased median is equal to the N/2 - Bias element of the stack:
biased_median(I[k]) = I[N/2 - Bias]
OUTPUTS
-
Box Outlier Output FITS
(interp_*_box_outlier.fits): The product of this step is an
outlier map. The pixel value is the deviation of the pixel in the
input image from the mean of that pixel in the stack, in terms of the
number of sigma.
DISCUSSION
This module represents a more complicated dual spatial-temporal filtering. It extends the regular temporal outlier detection, which computes the statistics of the pixels in the stack for each mosaic pixel position, by including the pixels in the box region around that pixel in the statistics. This allows for detecting outliers even in the coverage = 2 case and at the same time provides a guard against detecting pixels inside bright point sources as outliers.| Contents | ~ | Previous: Mosaic Modules: Mosaic Outlier | ~ | Next: Mosaic Modules: Mosaic RMask |
