Problems in geometric modelling and
perceptual grouping of man-made objects in aerial images
Research on man-made object recognition in satellite and aerial images has been
published for several decades now. Frequently model based approaches for the
recognition of roads and buildings are proposed. Models are described in terms of
relations of parts forming hierarchical arrangements.
The recognition process then consists of corresponding substeps of perceptual groupings,
searching for constructions consistent with the model.
This approach has been proposed for quite different recognition tasks.
Detection (i. e. searching for special objects), classification
(i. e. assigning object class labels to data objects) and even reconstruction
of all objects in a scene have been investigated. Data may have been acquired by
different sensors (e. g. visible light, IR, SAR or laser range data).
Perspectives vary from very oblique to perpendicular.
Objects of interest may be fixed in position (e. g. buildings) as well as
moving around (e. g. vehicles or containers).
Buildings may have numerous variations in shape whereas vehicles may be categorised
into quite homogenous classes.
Civil tasks differ frequently from military tasks in the type of objects,
sensors and perspectives used. This implies distinct object descriptions and
strategies of modelling and searching.
The euphoric activism of the 80s concerning the application of model based
approaches to automatic photogrammetry, remote sensing and cartography has
decreased during the last years.
This results not from a saturation process settling the research
on secure ground and common views.
On the contrary, there seems to be resignation because of practical problems
with structural models and lack of success in urban scenery.
This paper discusses typical problems in the construction and application
of such grouping approaches to man-made object recognition in aerial images.
There are different strategies in decomposing object aggregates into object parts.
A simple parallelogram shape may for instance be constructed from an angle pair
or a pair of parallel line pairs.
We demonstrate implications on the search performance resulting from distinct
modelling strategies by two examples.
Often there is a functional aspect of modelled parts exploitable for
recognition guidance. Also different possible representational schemes
like semantic nets and production systems are presented.
Complex models with many degrees of freedom permit classical one-step
template matching (with tolerable effort) only if there is
enough prior knowledge (e. g. from maps).
Otherwise perceptual grouping of object parts seems to be the only
reasonable alternative. This gives best results
if an alternation is implemented between grouping and matching.
Generic models pose the most severe performance problems because of their
inherent combinatorics. This is discussed with examples of grouping buildings
into settlement structures. Two main types of grouping are distinguished - cyclical
and cycle free - with implications on the associated computational effort.
Frequently used grouping structures are line prolongation for roads and
rectilinear pairs or triples for buildings.
Important geometrical relations are therefore collinearity, parallelity,
vicinity e. c. Many systems use inverting techniques
(like content addressable retrieval) for some such relations,
that help accelerating the search for permissible group partners in the database.
We also give two simple examples where relation inverting is impractical.
For tasks with very huge data amounts some interdisciplinary fertilisation
with databanking is helpful.
Michaelsen E, Stilla U (2000)
Problems in geometric modelling and perceptual grouping
of man-made objects in aerial images.
International Archives of Photogrammetry and Remote Sensing.
Vol. 33, Part B3, 577-583
[ Stilla.de/pub ]