References
- 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].
- Biuro Urządzenia Lasu i Geodezji Leśnej 2009. Available from: <http://www.buligl.pl/web/biuro-urzadzania-lasu-en/home>.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Goetz, AFH 2009, ‘Three decades of hyperspectral remote sensing of the Earth: A personal view’, Remote Sensing of Environment, vol. 113, pp. S5-S16.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Pal, M & Mather, PM 2006, ‘Some issues in the classification of DAIS hyperspectral data’. International Journal of Remote Sensing, vol. 27, pp. 2895-2916.
- 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.
- 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.
- 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.
- 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.
- 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.