Review of current and new supervised classification methods of satellite imagery with increased attention to the knowledge base classification (case study: Montagne Noir - France)

Ekologia Bratislava

Volumn 22, Issue SUPPL. 2, 2003, Pages 168-181

  Král, Kamil ^a  

^a MENDEL UNIVERSITY IN BRNO   (Czech Republic)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION; KNOWLEDGE BASED SYSTEM; LANDSCAPE ECOLOGY; REMOTE SENSING; SATELLITE IMAGERY;

EID: 2342632564     PISSN: 1335342X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (22)

1. Bishop, Y. et al., 1975: Discrete Multivariate Analysis - Theory and Practice. MIT Press, Cambridge, MA.
2. Brown, N., Gerard, F., Fuller, R., 2002: Mapping of land use classes Within the CORINE. The Cartographic Journal, 39, 1, p. 5-14.
3. Conseil International De La Langue Francaise, 1997: Terminologie de Teledetection et Photogrammetrie, Francais - Anglais, Paris, France.
4. rd revised edition, edition RIALP, S.A., Madrid, Spain, 452 pp.
5. Cohen, J., 1960: A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 1, p. 37-40.
6. Congalton, R., 1991: A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment, 37, p. 35-46.
7. ERDAS Inc., 1999: ERDAS Field Guide and Tour Guide - fifth edition, Atlanta, Georgia, USA, 536 pp.
8. Fenstermarker, L., 1991: A proposed approach from national to global scale error assessments. In Proceedings GIS/LIS '91, ASPRS, ACSM, AAG, AM/FM. International and URISA, 1, p. 293-300.
9. Foody, G.M., 1996: Approaches for the production and evaluation of fuzzy land cover classifications from remotely sensed data. International Journal of Remote Sensing, 17, p. 1317-1340.
10. Franklin, J. et al., 1986: Coniferous forest classification and inventory using Landsat and digital terrain data, IEEE Transactions on Geoscience and Remote Sensing, 24, 1, p. 139-149.
11. Franklin, J., 1995: Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography, 19, p. 494-519.
12. Goodchild, M.F., 1994: Integrating GIS and remote sensing for vegetation analysis and modeling: methodological issues. Journal of Vegetation Science, 5, p. 615-626.
13. Jansen, L.L.F. et al., 1990: Integrating topographic data with remote sensing for land-cover classification. Photogrammetric Engineering and Remote Sensing, 56, p. 1503-1506.
14. Maselli et al., 1994: Integration of ancillary data into a Maximum Likelihood Classifier with nonparametric priors. ISPRS Journal of Photogrammetry and Remote Sensing, 50, p. 2-11.
15. Naveh, Z., Lieberman, A.S., 1984: Landscape Ecology - Theory and application, Springer-Verlag, New York, 356 pp.
16. Rosenfield, G., Fitfpatrick-Lins, K., 1986: A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering and Remote Sensing, 52, 2, p. 223-227.
17. Shrestha, D.P., Zinck, A., 2001: Land use classification in mountainous areas: integration of image processing, digital elevation data and field knowledge (application to Nepal). JAG, 3, 1, p. 78-85.
18. SPIN, 2000: Spatial Indicators for European Nature Conservation Project - Proposal Number: EVG2-200-00512.
19. Stehman, S.V., 1992: Comparison of systematic and random sampling for estimating the accuracy of maps generated from remotely sensed data. Photogrammetric Engineering and Remote Sensing, 58, p. 1343-1350.
20. Stuckens, J., Coppin, P.R., Bauer M.E., 2000: Integrating Contextual Information with per-Pixel Classification for Improved Land Cover Classification, Remote Sensing of Environment, 71, p. 282-296.
21. Troll, C., 1939: Luftbildplan und ökologishe Bodenforshung. Zeitschrift der Ges. Für Erdkunde, Berlin, 7/8, p. 241-298.
22. Wang, F., 1990: Improving remote sensing image analysis through fuzzy classification representation. Photogrammetric Engineering and Remote Sensing, 56, p. 1163-1169.