Optimization at multi-spectral land cover classification with fuzzy clustering and the Kohonen feature map

International Journal of Remote Sensing

Volumn 22, Issue 8, 2001, Pages 1423-1439

  Poth, A ^a   Klaus, D ^a   Voss M ^a   Stein, G ^b  

^a UNIVERSITY OF BONN   (Germany)

^b Forschungszentrum Jülich ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; OPTIMIZATION; SPECTRUM ANALYZERS;

MULTI-SPECTRAL IMAGES;

REMOTE SENSING;

ALGORITHM; FUZZY MATHEMATICS; LAND CLASSIFICATION; MULTISPECTRAL IMAGE; OPTIMIZATION;

FRANCE; LANGUEDOC-ROUSSILLON; PERPIGNAN;

EID: 0035918555     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431160117710     Document Type: Article

References (28)

1. Bartelme, N., 1995, Geoinformatik (Berlin/Heidelberg: Springer).
2. Bezdek, J. C., Ehrlich, R., and Full, W., 1984, The fuzzy c-means clustering algorithm. Computers and Geoscience, 10, 191-223.
3. BOCK, ST., 1995, Ein Ansatz zur polygonbasierten Klassifikation von Luft-und Satellitenbildern mittels künstlicher neuronaler Netze. Kieler Geographische Schriften Bd. 91.
4. Bruzzone, L., Conese, C., Maselli, F., and Roli, F., 1997, Multisource classification of complex rural areas by statistical and neural-network approaches. Photogrammetric Engineering and Remote Sensing, 5, 523-533.
5. Buhmann, J., and Kühnel, H., 1993, Complexity optimized data clustering by competitive neural networks. Neural Computation, 5, 75-88.
6. Canty, M. J., 1999, Fernerkundung mit neuronalen Netzen (Renningen-Malmsheim: Expert Verlag).
7. Ceballos, J. C., and Bottino, M. J., 1997, The discrimination of scenes by principal components analysis of multi-spectral Imagery. International Journal of Remote Sensing, 11, 2437-2449.
8. Chuvieco, E., and Congalton, R. G., 1998, Using cluster analysis to improve the selection of training statistics in classifying remotely sensed data. Photogrammetric Engineering and Remote Sensing, 9, 1275-1281.
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, 7, 1317-1340.
10. Foody, G. M., and Cox, D. P., 1994, Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions. International Journal of Remote Sensing, 3, 619-632.
11. Gath, I., and Geva, A. B., 1989, Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 761-766.
12. Grignetti, A., Salvatori, R., Casaccha, R., and Manes, F., 1997, Mediterranean vegetation analysis by multi-temporal satellite sensor data. International Journal of Remote Sensing, 6, 1307-1318.
13. Grob, M. H., and Seibert, F., 1993, Visualization of multidimensional image data sets using a neural network. The Visual Computer, 10, 145-159.
14. Hölzer, m., 1998, Am Anfang war das Perzeptron. Toolbox, 2, 49-53.
15. HSIEH, P. F., and LANDGREBE, D., 1998, Classification of high dimensional data. Technical Report TR-ECE 98-4, May 1998, School of Electrical and Computer Engineering, Purdue University, West Lafayette.
16. Ito, Y., and Omatu, S., 1997, Category classification method using a self-organizing neural network. International Journal of Remote Sensing, 4, 829-845.
17. Klaus, D., Canty, M., Poth, A., Vob, M., Stein, G., and Niemeyer, I., 1999, Satellitenbildauswertung mit künstlichen Neuronalen Netzen zur Umweltüberwachung. Vergleichende Bewertung statistischer und Neuronaler Netzwerkalgorithmen und Entwicklung eines integrierten Verfahrens (Julich: Forschungszentrum Jülich).
18. Klaus, D., Poth, A., and Vob, M., 1997, Comparative investigation of neural network and statistical classification methods in geographical remote sensing and possible applications of safeguard. Proceedings of Workshop on Science and Modern Technology for Safeguards, Arona, Italy, 28-31 October 1996 (Brussels/Luxembourg: ECSC-EC-EAEC), pp. 265-273.
19. KLAWONN, F., and KRUSE, R., 1996, Derivation of fuzzy classification rules from multidimensional data. http://info.et-info.fho-emden.de/-dmlab/
20. Kohonen, T., 1982, Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59-69.
21. Krakow, P. K., and Steinnocher, K., 1994, Auswahl eines optimalen Datensatzes fur die multispektrale Klassifizierung unter Einbeziehung von Texturmerkmalen. Zeitschrift für Photogrammetrie und Fernerkundung, 4, 116-122.
22. LIU, H. Q., and HUETE, A., 1994, A systems based modification of the ndvi to minimize soil and atmospheric noise. http://stargate.jpl.nasa.gov:1083/html/authorl.html
23. Richards, J. A., 1996, Remote Sensing: Digital Image Analysis, 2nd edn (Berlin/New York: Springer).
24. Sandmeier, S., 1997, Radiometrische Korrektur des Topographieeffekts in optischen Satellitenbilddaten—Vergleich eines semi-empirischen Verfahrens mit einem physikal-isch-basierten Modell. Photogrammetrie-Fernerkundung-Geoinformation, 1, 23-32.
25. Smith, M. O., Ustin, S. L., Adams, J. B., and Gillespie, A. R., 1990, Vegetation in deserts: I. A regional measure of abundance from multispectral images. Remote Sensing of Environment, 31, 1-26.
26. TOLIAS, Y. A., and PANAS, ST. M., 1997, An adaptaive fuzzy clustering algorithm for image segmentation. http://psyche.ee.auth.gr/spsplab/tolias/3iwsip/3iwsip.html
27. WAGNER, TH., and WAGNER, O., 1995, Eine graphische Oberflache fur ein Fuzzy-Datenanalyse-Programm und Datenanalyse mittels Fuzzy-Clustering. Dipl. Arbeit (Braunshweig).
28. Waldemark, J., 1997, An auotmated procedure for cluster analysis of multivariate satellite data. International Journal of Neural Systems, 1, 3-15.