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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_probability_map

arg_classification

Boolean

False

enable the probability map generation

## Parameters compatibility

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

Warning

if enable_probability_map is True then classifier must be 'sharkrf'

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

### Tests

UnitTests.ClassificationsTests.test_reorder_proba_map

UnitTests.OpticalSARFusionTests.test_compute_probamap_fusion