TY - JOUR

T1 - Use of Bayesian networks as judgement calculus in a knowledge based image interpretation system

AU - Growe, Stefan

AU - Schröder, Torsten

AU - Liedtke, C. E.

N1 - Publisher Copyright: © 2000 International Society for Photogrammetry and Remote Sensing. All rights reserved.

PY - 2000

Y1 - 2000

N2 - The increasing amount of remotely sensed imagery from multiple platforms requires efficient analysis techniques. The presented image interpretation system tries to automate the analysis of multisensor and multitemporal images by the use of structural, topological, and temporal knowledge about the objects expected in the scene. The knowledge base is formulated by a semantic net. Temporal knowledge about object states and their transitions is represented in a state transition graph which is integrated within the semantic net. The analysis of multitemporal images is improved by the prediction of possible object states derived from the knowledge base. During analysis the system has to deal with uncertainty and imprecision. Competing interpretations have to be judged to succeed with the most promising alternative. For this reason the measured object properties are compared to the expected ones. A probabilistic judgement calculus based on Bayesian networks is presented which uses the rules of belief updating and propagation. The approach integrates the probabilities of object states and their transitions within the judgement procedure. Hence it is well suited for a multitemporal image interpretation. For an example dealing with the detection of an industrial fairground from a set of aerial images the probabilistic judgement is compared with an existing possibilistic approach. It is shown, that the use of Bayesian networks increases the efficiency of the interpretation process.

AB - The increasing amount of remotely sensed imagery from multiple platforms requires efficient analysis techniques. The presented image interpretation system tries to automate the analysis of multisensor and multitemporal images by the use of structural, topological, and temporal knowledge about the objects expected in the scene. The knowledge base is formulated by a semantic net. Temporal knowledge about object states and their transitions is represented in a state transition graph which is integrated within the semantic net. The analysis of multitemporal images is improved by the prediction of possible object states derived from the knowledge base. During analysis the system has to deal with uncertainty and imprecision. Competing interpretations have to be judged to succeed with the most promising alternative. For this reason the measured object properties are compared to the expected ones. A probabilistic judgement calculus based on Bayesian networks is presented which uses the rules of belief updating and propagation. The approach integrates the probabilities of object states and their transitions within the judgement procedure. Hence it is well suited for a multitemporal image interpretation. For an example dealing with the detection of an industrial fairground from a set of aerial images the probabilistic judgement is compared with an existing possibilistic approach. It is shown, that the use of Bayesian networks increases the efficiency of the interpretation process.

KW - Bayesian network

KW - Image interpretation

KW - Multitemporal analysis

KW - Semantic net

UR - http://www.scopus.com/inward/record.url?scp=85030968599&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85030968599

VL - 33

SP - 342

EP - 350

JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

SN - 1682-1750

T2 - 19th International Congress for Photogrammetry and Remote Sensing, ISPRS 2000

Y2 - 16 July 2000 through 23 July 2000

ER -