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Probability map

Probability maps are rasters containing as bands as class. Each band reprensents the probability of belonging to a given class in [0; 1000] range. Bands are ordered from the lower to the greater integer label.

Parameters involved

Parameter Key

Parameter section

Parameter Type

Default value

Parameter purpose





enable the probability map generation

Parameters compatibility

The probability maps can only be generated if the shark classifier is used during the run.


if enable_probability_map is True then classifier must be 'sharkrf'

Additionnal outputs

Each classifications will generate it own probability map called PROBAMAP_TT_model_MM_seed_SS.tif in classif output iota2 directory where TT is the tile’s name MM the model’s name and SS the seed number. Once all generated, they are merged under the ProbabilityMap_seed_SS.tif name into the final directory.

Internal choices

In some case, there is many classifier decisions to a given pixel. This section detail internal choices in order to provide a probability map without NoData labels.

Post-classification fusion

By enabling the dempster_shafer_SAR_Opt_fusion parameter flag, iota2 will class each pixels invoking the model built thanks to SAR data and the one built by using optical data. Then, the dempster-shafer is used to attribute the final decision.

Here is the rules to attribute the vector of probability to each pixels

Consider :

\(p\): the pixel of interest

\(ProbaMapSAR\): the probability map provided by the SAR model

\(ProbaMapOpt\): the probability map provided by the optical model

\(ProbaMapOut\): the output probability map

\(DS\): the dempster-shafer choice

\(DS(p)\) in \({0, 1, 2, 3}\)

0 : no decision

1 : choice is both

2 : choice is SAR

3 : choice is Optical

Then :

\(ProbaMapOut(p) = 0\) if \(DS(p) = 0\)

\(ProbaMapOut(p) = ProbaMapSAR(p)\) if \(DS(p) = 2\)

\(ProbaMapOut(p) = ProbaMapOpt(p)\) if \(DS(p) = 3\)

\(ProbaMapOut(p) = ProbaMapOpt(p)\) if \(DS(p) = 1\) and \(max(ProbaMapOpt(p)) > max(ProbaMapSAR(p))\)

\(ProbaMapOut(p) = ProbaMapSAR(p)\) if \(DS(p) = 1\) and \(max(ProbaMapSAR(p)) > max(ProbaMapOpt(p))\)

Too huge regions

Using both parameters classifMode to 'separate' and mode_outside_RegionSplit to an integer value, many models will be built to a given region. Then each models will votes to each pixels inside the region and some fusion rules are involved. Here is the probability maps fusion rule :

the probability of a given class is the mean of all probability provided by each classifier.

Developers corner

Some unittests are involved by probability maps generation