A key issue in mission planning for aerial reconnaissance is to use the sensor ressources in an appropriate way. The sensor mission planning requires knowledge, e.g., about the optimal sensor type (IR/EO) or the necessary flying altitude for a specific task. There are various types of task that can be part of the mission, e.g., to detect a vehicle or investigate a bridge. The goal of this work is to examine knowledge-based approaches like ontologies and the use of them to automatically derive all needed parameters for an optimal sensor mission planning based on the task. The task-oriented mission planning is processed on the tactical level. Based on the task the aerial image analyst defines the specific evaluation conditions. Various parameters are part of the task-oriented mission planning. For example there are the scene, the flight mode, the sensor, the system and the image analysis parameters. We introduce an idea to represent the sensor mission planning task at a relatively high-level knowledge-based approach. We want to create a representation of a useful sensor mission planning where all aspects and its components are considered, what these parts do, how they relate to each other and define the rules and constraints within. The main framework is based on the definitions of target categories and purpose codes (STANAG 3596) and NIIRS level.
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.
“Although we know that it is not a familiar object, after a while we can say what it resembles”. The core task of an aerial
image analyst is to recognize different object types based on certain clearly classified characteristics from aerial or satellite
images. An interactive recognition assistance system compares selected features with a fixed set of reference objects (core
data set). Therefore it is mainly designed to evaluate durable single objects like a specific type of ship or vehicle. Aerial
image analysts on missions realized a changed warfare over the time. The task was not anymore to classify and thereby
recognize a single durable object. The problem was that they had to classify strong variable objects and the reference set
did not match anymore. In order to approach this new scope we introduce a concept to a further development of the
interactive assistance system to be able to handle also short-lived, not clearly classifiable and strong variable objects like
for example dhows. Dhows are the type of ships that are often used during pirate attacks at the coast of West Africa. Often
these ships were build or extended by the pirates themselves. They follow no particular pattern as the standard construction
of a merchant ship. In this work we differ between short-lived and durable objects. The interactive adaptable assistance
system is supposed to assist image analysts with the classification of objects, which are new and not listed in the reference
set of objects yet. The human interaction and perception is an important factor in order to realize this task and achieve the
goal of recognition. Therefore we had to model the possibility to classify short-lived objects with appropriate procedures
taking into consideration all aspects of short-lived objects. In this paper we will outline suitable measures and the
possibilities to categorize short-lived objects via simple basic shapes as well as a temporary data storage concept for shortlived
objects. The interactive adaptable approach offers the possibility to insert the data (objects) into the system directly
and on-site. To mitigate the manipulation risk the entry of data (objects) into the main reference (core data) set is granted
to a central authorized unit.