Uneingeschränkter Zugang

Mapping vegetation communities of the Karkonosze National Park using APEX hyperspectral data and Support Vector Machines


Zitieren

Benediktsson, JA & Waske, B 2009, ‘Next Frontier for Classification Tools: SVM and Beyond, 3rd HYPER-I-NET School on Hyperspectral Imaging’, Data Processing: from hyperspectral images to information, Pavia. Available from: http://hyperinet.multimediacampus.it/images/Benediktsson.pdf>. [8-11 September 2009].Search in Google Scholar

Biuro Urządzenia Lasu i Geodezji Leśnej 2009. Available from: <http://www.buligl.pl/web/biuro-urzadzania-lasu-en/home>.Search in Google Scholar

Burges, CJC 1998, ‘A tutorial on support vector machines for pattern recognition, data mining and knowledge discovery’, Kluwer Academic Publishers, vol. 2, pp. 121-167.10.1023/A:1009715923555Search in Google Scholar

Camps-Valls, G, Gomez-Chova, L, Calpe-Maravilla, J, Martin- Guerrero, JD, Soria-Olivas, E, Alonso-Chorda, L & Moreno, J 2004, ‘Robust support vector method for hyperspectral data classification and knowledge discovery’, IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 7, pp. 1530-1542.10.1109/TGRS.2004.827262Search in Google Scholar

Chan, JCW, Beckers, P, Spanhove, T & Vanden Borre, T 2012, ‘An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery’, International Journal of Applied Earth Observation and Geoinformation, vol. 18, pp. 13-22.10.1016/j.jag.2012.01.002Search in Google Scholar

Dalponte, M, Bruzzone, L & Gianelle, D 2008, ‘Fusion of hyperspectral and LIDAR Remote sensing data for classification of complex forest areas’, IEEE Transactions On Geoscience and Remote Sensing, vol. 46, no. 5, pp. 1416-1427.10.1109/TGRS.2008.916480Search in Google Scholar

Delalieux, S, Somers, B, Haest, B, Kooistra, L, Mücher, CA & Vanden Borre, J 2010, ‘Monitoring heathland habitat status using hyperspectral image classification and unmixing’, Proceedings of the 2nd Whispers on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), IEEE GRSS, University of Iceland, Reykjawik, pp. 50-54.10.1109/WHISPERS.2010.5594895Search in Google Scholar

Dixon, B & Candade, N 2008, ‘Multispectral landuse classification using neural networks and support vector machines: one or the other, or both?’, International Journal of Remote Sensing, vol. 29, no. 4, pp. 1185-1206.10.1080/01431160701294661Search in Google Scholar

Goetz, AFH 2009, ‘Three decades of hyperspectral remote sensing of the Earth: A personal view’, Remote Sensing of Environment, vol. 113, pp. S5-S16.10.1016/j.rse.2007.12.014Search in Google Scholar

Gualtieri, JA & Cromp, RF 1998, Support vector machines for hyperspectral remote Sensing classification, Proceedings of the 27th AIPR Workshop’, Advances in Computer Assisted Recognition, pp. 221-232.10.1117/12.339824Search in Google Scholar

Huang, C, Davis, LS & Townshend, JRG 2002, ‘An assessment of support vector machines for land cover classification’, International Journal of Remote Sensing, vol. 23 pp. 725-749.10.1080/01431160110040323Search in Google Scholar

Itten, KI, Dell’Endice, F, Hueni, A, Kneubühler, M, Schläpfer, D, Odermatt, D, Seidel, D, Huber, S, Schopfer, J, Kellenberger, T, Bühler, Y, D’Odorico, P, Nieke, J, Alberti, E & Meuleman, K 2008, ‘APEX - the hyperspectral ESA airborne prism experiment’, Sensors, vol. 8, pp. 6235-6259.10.3390/s8106235370744827873868Search in Google Scholar

Kokaly, RF, Despain, DG, Clark, RN & Livo, KE 2003, ‘Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data’, Remote Sensing of Environment, vol. 84, pp. 437-456.10.1016/S0034-4257(02)00133-5Search in Google Scholar

Olesiuk, D, Bachmann, M, Habermeyer, M, Heldens, W & Zagajewski, B 2009, ‘Crop classification with neural networks using airborne hyperspectral imagery’, Roczniki Geomatyki, vol. VII, no. 32, pp. 107-112.Search in Google Scholar

Pal, M & Mather, PM 2004, ‘Assessment of the effectiveness of support vector machines for hyperspectral data’, Future Generation Computer Systems, vol. 20, no. 7, pp. 1215-1225.10.1016/j.future.2003.11.011Search in Google Scholar

Pal, M & Mather, PM 2006, ‘Some issues in the classification of DAIS hyperspectral data’. International Journal of Remote Sensing, vol. 27, pp. 2895-2916.10.1080/01431160500185227Search in Google Scholar

Szymura, TH, Dunajski, A, Aman, I, Makowski, M, & Szymura, M 2007, ‘The spatial pattern and microsites requirements of Abies alba natural regeneration in the Karkonosze Mountains’, Dendrobiology, vol. 58, pp. 51-57.Search in Google Scholar

Wojtuń, B, Żołnierz, L & Raj, A 2004, ‘Nowy operat ochrony ekosystemów nieleśnych Karkonoskiego Parku Narodowego, Geoekologické problémy Krkonoš’, Opera Corcontica, vol. 41, pp. 560-567.Search in Google Scholar

Zagajewski, B 2010, ‘Ocena przydatności sieci neuronowych i danych hiperspektralnych do klasyfikacji roślinności Tatr Wysokich’ (Assessment of neural networks and Imaging Spectroscopy for vegetation classification of the High Tatras), Teledetekcja Środowiska, vol. 43.Search in Google Scholar

Zagajewski, B & Sobczak, M 2003, ‘Field remote sensing techniques for mountains vegetation investigation’, Proceedings of the 3rd EARSeL Workshop on Imaging Spectroscopy, Oberpfaffenhofen, pp. 580-596.Search in Google Scholar

Zagajewski, B, Kozłowska, A, Krówczyńska, M, Sobczak, M & Wrzesień, M 2005, ‘Mapping high mountain vegetation using hyperspectral data’. EARSeL eProceedings, vol. 4, no. 1, pp. 70-78. Search in Google Scholar

eISSN:
2084-6118
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
4 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Geowissenschaften, Geografie, andere