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 set Use Dual Outlier For Rmask in the Mosaic RMask module and set the RMask Fatal Mask Bit Pattern in the Initial Setup module to use bit 2. Note that this is not the same as setting it to a value of 2. See §8.11: Fatal Mask Bit Patterns for more information.
PURPOSE
This module takes the outputs from Detect and Mosaic Projection and applies the user-input criteria to classify outliers using both spatial and temporal information.
INPUT
Max Outlier Image: (int) The maximum number of images in which a potential outlier can be present and still be declared a dual outlier. Default value is 2. This value should be smaller than the total number of coverage at each pixel.
Max Outlier Fraction: (float) This parameter specifies the maximum fraction of the images in which a potential outlier can be present and still be declared a dual outlier, i.e. the number of images containing outlier pixels divided by the total stack of input BCD images.
Tile X(Y) Size: (int) The X (Y) size of an image tile used for this module. When the number of input images is very large, the recommended option is to use smaller Tile size than the total number of pixels of the final output mosaic image, in order to avoid memory shortage. The mosaic may be broken up into tiles in order to avoid memory allocation problems if the mosaic is very large (see §8.1).
Dual Outlier output subdirectory: The subdirectory of <output_dir> that you wish to use for the output files. Default is DualOutlier-mosaic.
COMMAND LINE INPUT
&MOSAICDUALOUTLIERIN
MAX_OUTL_IMAGE = 2,
MAX_OUTL_FRAC = 0.95,
TILE_XSIZ = 500,
TILE_YSIZ = 500,
&END
In Global Parameters:
DUAL_OUTLIER_DIR = DualOutlier-mosaic
OUTPUT
Dual Outlier Images (proj_*_detmap_dual_outlier.fits): The output images with outlier pixels flagged. Outliers have negative values and real sources have positive values.
DISCUSSION
Outlier rejection employs a complicated set of algorithms. The basic concept of Dual Outlier detection is to use both spatial and temporal filtering method. If you do not have good coverage or you want to make sure the edges of your mosaic image are clean of outliers, you should use this option. See §8.2.3 for full details of Dual Outlier Detection.
The output outlier maps have likely outliers flagged on a pixel-by-pixel basis. However, clusters may contain both positive and negative identifications. The Levelmodule next determines whether each cluster should be considered an outlier or not in its entirety.
