Details
| Original language | English |
|---|---|
| Pages (from-to) | 811-821 |
| Number of pages | 11 |
| Journal | Photogrammetric Engineering and Remote Sensing |
| Volume | 65 |
| Issue number | 7 |
| Publication status | Published - Jul 1999 |
Abstract
The increasing amount of remotely sensed imagery requires efficient analysis techniques. The leading idea of the presented work is to automate the interpretation of aerial images by the use of common a priori knowledge about landscape scenes. In addition, the system uses specific map knowledge of a GIS. The a priori knowledge about landscape scenes, the aerial images, and the image forming sensors is represented explicitly by a semantic net. The definition of a network language allows the exploition of the knowledge base by a set of application-independent rules which provide data and model-driven control strategies. Competing interpretations are stored in a search tree and judged considering their uncertainty and imprecision. An A*-algorithm selects the most promising interpretation for further analysis. Results are shown for the extraction of roads and complex objects, such as purification plants, from multisensor imagery.
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
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In: Photogrammetric Engineering and Remote Sensing, Vol. 65, No. 7, 07.1999, p. 811-821.
Research output: Contribution to journal › Review article › Research › peer review
}
TY - JOUR
T1 - Knowledge-based interpretation of remote sensing images using semantic nets
AU - Tönjes, R.
AU - Growe, S.
AU - Bückner, J.
AU - Liedtke, C. E.
PY - 1999/7
Y1 - 1999/7
N2 - The increasing amount of remotely sensed imagery requires efficient analysis techniques. The leading idea of the presented work is to automate the interpretation of aerial images by the use of common a priori knowledge about landscape scenes. In addition, the system uses specific map knowledge of a GIS. The a priori knowledge about landscape scenes, the aerial images, and the image forming sensors is represented explicitly by a semantic net. The definition of a network language allows the exploition of the knowledge base by a set of application-independent rules which provide data and model-driven control strategies. Competing interpretations are stored in a search tree and judged considering their uncertainty and imprecision. An A*-algorithm selects the most promising interpretation for further analysis. Results are shown for the extraction of roads and complex objects, such as purification plants, from multisensor imagery.
AB - The increasing amount of remotely sensed imagery requires efficient analysis techniques. The leading idea of the presented work is to automate the interpretation of aerial images by the use of common a priori knowledge about landscape scenes. In addition, the system uses specific map knowledge of a GIS. The a priori knowledge about landscape scenes, the aerial images, and the image forming sensors is represented explicitly by a semantic net. The definition of a network language allows the exploition of the knowledge base by a set of application-independent rules which provide data and model-driven control strategies. Competing interpretations are stored in a search tree and judged considering their uncertainty and imprecision. An A*-algorithm selects the most promising interpretation for further analysis. Results are shown for the extraction of roads and complex objects, such as purification plants, from multisensor imagery.
UR - http://www.scopus.com/inward/record.url?scp=18344400135&partnerID=8YFLogxK
M3 - Review article
AN - SCOPUS:18344400135
VL - 65
SP - 811
EP - 821
JO - Photogrammetric Engineering and Remote Sensing
JF - Photogrammetric Engineering and Remote Sensing
SN - 0099-1112
IS - 7
ER -