This paper introduces an interactive recognition assistance system for imaging reconnaissance. This system supports
aerial image analysts on missions during two main tasks: Object recognition and infrastructure analysis. Object
recognition concentrates on the classification of one single object. Infrastructure analysis deals with the description of
the components of an infrastructure and the recognition of the infrastructure type (e.g. military airfield). Based on satellite
or aerial images, aerial image analysts are able to extract single object features and thereby recognize different object
types. It is one of the most challenging tasks in the imaging reconnaissance. Currently, there are no high potential ATR
(automatic target recognition) applications available, as consequence the human observer cannot be replaced entirely.
State-of-the-art ATR applications cannot assume in equal measure human perception and interpretation. Why is this still
such a critical issue? First, cluttered and noisy images make it difficult to automatically extract, classify and identify
object types. Second, due to the changed warfare and the rise of asymmetric threats it is nearly impossible to create an
underlying data set containing all features, objects or infrastructure types. Many other reasons like environmental
parameters or aspect angles compound the application of ATR supplementary. Due to the lack of suitable ATR
procedures, the human factor is still important and so far irreplaceable. In order to use the potential benefits of the human
perception and computational methods in a synergistic way, both are unified in an interactive assistance system.
RecceMan® (Reconnaissance Manual) offers two different modes for aerial image analysts on missions: the object
recognition mode and the infrastructure analysis mode. The aim of the object recognition mode is to recognize a certain
object type based on the object features that originated from the image signatures. The infrastructure analysis mode
pursues the goal to analyze the function of the infrastructure. The image analyst extracts visually certain target object
signatures, assigns them to corresponding object features and is finally able to recognize the object type. The system
offers him the possibility to assign the image signatures to features given by sample images. The underlying data set
contains a wide range of objects features and object types for different domains like ships or land vehicles. Each domain
has its own feature tree developed by aerial image analyst experts. By selecting the corresponding features, the possible
solution set of objects is automatically reduced and matches only the objects that contain the selected features.
Moreover, we give an outlook of current research in the field of ground target analysis in which we deal with partly
automated methods to extract image signatures and assign them to the corresponding features. This research includes
methods for automatically determining the orientation of an object and geometric features like width and length of the
object. This step enables to reduce automatically the possible object types offered to the image analyst by the interactive
recognition assistance system.