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Pakzad, Kian [Author]; Heipke, Christian [Author]; Remetey-Fülöpp, Gabor [Author]; Clevers, Jan [Author]; Beek, Klaas Jan [Author]

Knowledge based interpretation of moorland in aerial images - [published Version]

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  • Media type: E-Article; Text
  • Title: Knowledge based interpretation of moorland in aerial images
  • Contributor: Pakzad, Kian [Author]; Heipke, Christian [Author]; Remetey-Fülöpp, Gabor [Author]; Clevers, Jan [Author]; Beek, Klaas Jan [Author]
  • Published: London : International Society for Photogrammetry and Remote Sensing, 2000
  • Published in: XIXth ISPRS Congress. Technical Commission VII: Resource and Environmental Monitoring ; The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 33 Part B7
  • Issue: published Version
  • Language: English
  • DOI: https://doi.org/10.15488/3447
  • ISSN: 1682-1750
  • Keywords: Temporal change ; Konferenzschrift ; Semantics ; Monitoring ; Knowledge representation ; Remote sensing ; Explicit knowledge ; Object structure ; Knowledge based systems ; Antennas ; Model-based processing ; Change detection ; Multispectral classification ; Remote sensing data ; Multi-temporal ; Land use
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  • Description: For the interpretation of remote sensing data the traditional methods such as multispectral classification are in many cases not sufficient. This applies especially to more complex scenes. In order to interpret such scenes it is necessary to include and use more prior knowledge about the depicted objects, e.g. knowledge about the possible object structure or, in a multitemporal interpretation, knowledge about the possible temporal changes. In this paper we present an approach for the automatic interpretation of moorland from aerial images. The first step is a monotemporal interpretation. We use a knowledge based system with an explicit knowledge representation through semantic nets. This system is suitable to formulate explicitly (i.e. in a standard language) prior knowledge and to use it for the interpretation. In our case we divided moorland into different relevant land use classes and described them in a semantic net. For every class we described the obligatory parts. Obligatory parts are features and structures, which have to be detected in the particular areas in order to assign them the corresponding class. Because in moorland areas monitoring of changes is very important we extended the monotemporal system to a multitemporal one. The multitemporal interpretation also exploits explicitly represented prior knowledge about the possible temporal changes. The results show that the presented approach is suitable for the interpretation of moorland. The exploited additional prior knowledge led to an improvement of the interpretation, especially for the multitemporal one. © 2000 International Society for Photogrammetry and Remote Sensing. All rights reserved.
  • Access State: Open Access
  • Rights information: Attribution (CC BY)