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MOPEX User's Guide - IRSA

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<strong>MOPEX</strong> User’s <strong>Guide</strong><br />

This module does Multiframe Temporal Outlier detection (§8.2.2) on the interpolated input<br />

images. The interpolated input frames are stacked, and for each pixe l position in the<br />

interpolated grid, the trimmed mean and standard deviation of the pixel values in the stack is<br />

calculated. The pixel values outside of the user-specified asymmetrical multi-sigma<br />

thresholds are classified as outliers. This method detects both moving objects and radhits. It is<br />

not meant to be used in the cases of shallow coverage.<br />

The thresholds - Bottom Threshold and Top Threshold - can be set to 0, which is the default.<br />

In this case, the decision of declaring a particular pixel an outlier is put off until running the<br />

Mosaic RMask module. The advantage of doing it this way is that the user can experiment<br />

with different thresholds for outliers in the Mosaic RMask module, without having to rerun<br />

the Mosaic Outlier module.<br />

5.6.15 Mosaic Modules: Mosaic Box Outlier<br />

Command Line Equivalent: run_mosaic_box_outlier<br />

Default Output Directory: /BoxOutlier-mosaic<br />

De pe nds On: Mosaic Interpolate<br />

Important Notes: Using this module does not mean that <strong>MOPEX</strong> will automatically use the<br />

results for outlier detection. In order to use the results from this module, you must set Use Box<br />

Outlier For Rmask in the Mosaic RMask module and set the RMask Fatal Mask Bit Pattern in<br />

the Initial Setup module to use bit 3. Note that this is not the same as setting it to a value of 3. See<br />

§8.11: Fatal Mask Bit Patterns for more information.<br />

PURPOSE<br />

This module uses the Box Outlier rejection method to flag bad pixels (see §8.2.4). The<br />

method is designed to use both the temporal and spatial information like the Dual Outlier, but<br />

it uses the statistical analysis of the kind used by Multiframe Temporal Outlier. In the<br />

identification of temporal outliers, neighboring pixels are utilized to ensure the correct<br />

classification.<br />

INPUT<br />

Box Size X(Y) direction: (int) The X (Y) size of the box of neighboring pixels used for<br />

outlier detections.<br />

Mosaicking (mosaic.pl) 76<br />

Mosaic Modules

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