This framework is dedicated to perform cartographic validation starting from the result of a detection (for example a road extraction), enhance the results fiability by using a classifier fusion algorithm. Using a set of descriptor, the processing chain validates or invalidates the input geometrical features.
The DSFuzzyModelEstimation application performs the fuzzy model estimation (once by use case: descriptor set / Belief support / Plausibility support). It has the following input parameters :
The application can be used like this:
The output file FuzzyModel.xml contains the optimal model to perform informations fusion.
The first step in the classifier fusion based validation is to compute, for each studied polyline, the choosen descriptors. In this context, the ComputePolylineFeatureFromImage application can be used for a large range of descriptors. It has the following inputs :
The output is a vector data containing polylines with a new field containing the descriptor value. In order to add the ”NONDVI” descriptor to an input vector data (”inVD.shp”) corresponding to the percentage of pixels along a polyline that verifies the formula ”NDVI >0.4” :
NDVI.TIF is the NDVI mono band image of the studied scene. This step must be repeated for each choosen descriptor:
Both NDVI.TIF and roadSpectralAngle.TIF can be produced using Monteverdi feature extraction capabilities, and Buildings.TIF can be generated using Monteverdi rasterization module. From now on, VD_NONDVI_ROADSA_NOBUIL.shp contains three descriptor fields. It will be used in the following part.
The final application (VectorDataDSValidation) will validate or unvalidate the studied samples using the Dempster-Shafer theory . Its inputs are :
The output is a vector data containing only the validated samples.