Not all detected features have a segmented area associated with them. Here, we show two cases in which a detected feature might not have a segmented area associated with them (meaning that the mask file does not contain the ID of the feature of interest and ncells in the segmentation output dataframe results in 0 grid cells. )
If the segmentation threshold is lower (assuming target='minimum') than the highest threshold specified in the Feature Detection (see threshold_detection_parameters) this could leave some features without a segmented area, simply because there are no values to be segmented.
Consider for example the following data with 5 being the highest threshold specified for the Feature Detection (see feature_detection_overview):
If the segmentation threshold is larger than 5 (e.g. threshold = 6), the segmented area contains all values <= 5 (still assuming target='minimum'), no matter if the detected feature has a threshold lower than 5 (upper panels) or if it is exactly equal to 5 and does not contain any features with lower thresholds inside (lower panels).
If the segmentation threshold is lower than or equal to the highest feature detection threshold (e.g. threshold = 5), features with threshold values lower than 5 still get a segmented area associated with them (upper panels). However, features that are exactly equal to 5 and do not contain any features with lower thresholds inside will not get any segmented area associated with them (lower panels) which results in no values in the mask for this feature and ncells=0.
Another reason for features that do not have a segmented area associated with them is the rare but possible case when the feature position is located outside of the threshold area:
In this case, it may help to change the position_threshold (see threshold_detection_parameters) to extreme instead of `center`:
