  As established in previous papers of this series, observables in highly
distorted and magnified multiple images caused by the strong gravitational
lensing effect can be used to constrain the distorting properties of the
gravitational lens at the image positions. If the background source is extended
and contains substructure, like star forming regions, which is resolved in
multiple images, all substructure that can be matched across a minimum of three
multiple images can be used to infer the local distorting properties of the
lens. In this work, we replace the manual feature selection by an automated
feature extraction based on SExtractor for Python and show its superior
performance. Despite its aimed development to improve our lens reconstruction,
it can be employed in any other approach, as well. Valuable insights on the
definition of an `image position' in the presence of noise are gained from our
calibration tests. Applying it to observations of a five-image configuration in
galaxy cluster CL0024 and the triple-image configuration containing Hamilton's
object, we determine local lens properties for multiple wavebands separately.
Within current confidence bounds, all of them are consistent with each other,
corroborating the wavelength-independence of strong lensing and offering a tool
to detect deviations caused by micro-lensing and dust in further examples.
