museotoolbox.ai.SequentialFeatureSelection.predict_images

SequentialFeatureSelection.predict_images(in_image, out_image_prefix, in_image_mask=False, higher_confidence=False)[source]

Predict each best found features with SFFS.fit(X,y).

Parameters

  • in_image (str.) – Path of the raster to predict.

  • out_image_prefix (str.) – Prefix of each raster to save. Will add in suffix the iteration number then .tif. E.g. outRasterPrefix = classification_, will give classification_0.tif for the first prediction.

  • in_image_mask (str or False, optional (default=False)) – Path to the image mask where 0 values are masked data.

  • higher_confidence (False or str. Default False.) – If str, same as outRasterPrefix.