Details
| Originalsprache | Englisch |
|---|---|
| Fachzeitschrift | European Signal Processing Conference |
| Jahrgang | 1998-January |
| Publikationsstatus | Veröffentlicht - 1998 |
| Veranstaltung | 9th European Signal Processing Conference, EUSIPCO 1998 - Island of Rhodes, Griechenland Dauer: 8 Sept. 1998 → 11 Sept. 1998 |
Abstract
A knowledge based approach for the interpretation of aerial images is presented that combines cues from multiple sensors (visual, infrared, SAR). The sensor fusion is applied at object level. This allows to use prior knowledge to increase the separability of the classes. The prior knowledge is represented explicitly using semantic nets. Interpretation exploits the semantic net to control the sequence of sensor fusion mixing bottom-up and top-down strategies. The presented approach addresses the problem of uncertain and imprecise sensor data by judging the different cues based on possibility theory. Competing interpretations are stored in a search tree. An A∗-algorithm selects the most promising, i.e. best judged, interpretation for further investigation.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Signalverarbeitung
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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in: European Signal Processing Conference, Jahrgang 1998-January, 1998.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Knowledge based interpretation of aerial images using multiple sensors
AU - Tönjes, R.
AU - Liedtke, C. E.
PY - 1998
Y1 - 1998
N2 - A knowledge based approach for the interpretation of aerial images is presented that combines cues from multiple sensors (visual, infrared, SAR). The sensor fusion is applied at object level. This allows to use prior knowledge to increase the separability of the classes. The prior knowledge is represented explicitly using semantic nets. Interpretation exploits the semantic net to control the sequence of sensor fusion mixing bottom-up and top-down strategies. The presented approach addresses the problem of uncertain and imprecise sensor data by judging the different cues based on possibility theory. Competing interpretations are stored in a search tree. An A∗-algorithm selects the most promising, i.e. best judged, interpretation for further investigation.
AB - A knowledge based approach for the interpretation of aerial images is presented that combines cues from multiple sensors (visual, infrared, SAR). The sensor fusion is applied at object level. This allows to use prior knowledge to increase the separability of the classes. The prior knowledge is represented explicitly using semantic nets. Interpretation exploits the semantic net to control the sequence of sensor fusion mixing bottom-up and top-down strategies. The presented approach addresses the problem of uncertain and imprecise sensor data by judging the different cues based on possibility theory. Competing interpretations are stored in a search tree. An A∗-algorithm selects the most promising, i.e. best judged, interpretation for further investigation.
KW - GIS
KW - Interpretation
KW - Knowledge base
KW - Scene analysis
KW - Sensor fusion
UR - http://www.scopus.com/inward/record.url?scp=0009815902&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:0009815902
VL - 1998-January
JO - European Signal Processing Conference
JF - European Signal Processing Conference
SN - 2219-5491
T2 - 9th European Signal Processing Conference, EUSIPCO 1998
Y2 - 8 September 1998 through 11 September 1998
ER -