Keramitsoglou I, Sarimveis H, Kiranoudis CT, Kontoes C, Sifakis N, Fitoka E (2006) The
performance of pixel window algorithms in the classification of habitats using VHSR imagery.
ISPRS J Photogramm Remote Sens 60(4):225–238
Keuchel J, Naumann S, Heiler M, Siegmund A (2003) Automatic land cover analysis for Tenerife
by supervised classification using remotely sensed data. Remote Sens Environ 86(4):530–541
Liu T, Yang X (2013) Mapping urban vegetation using layered classification and multiple
endmember spectral mixture analysis. Remote Sens Environ 133:251–264
Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J
Photogramm Remote Sens 66(3):247–259
Pal M, Mather PM (2005) Support vector machines for classification in remote sensing. Int J
Remote Sens 26(5):1007–1011
Plaza A, Benediktsson JA, Boardman JW, Brazile J, Bruzzone L, Camps-valls G, Chanussot J,
Fauvel M, Gamba P, Gualtieri A, Marconcini M, Tilton JC, Trianni G (2009) Recent advances
in techniques for hyperspectral image processing. Remote Sens Environ 113(1):110–122
Richards JA, Landgrebe DA, Swain PH (1982) A means for utilizing ancillary information in
multispectral classifications. Remote Sens Environ 12(6):463–477
Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP (2012) An
assessment of the effectiveness of a random forest classifier for land-cover classification.
ISPRS J Photogramm Remote Sens 67:93–104
Schmidt KS, Skidmore AK, Kloosterman EH, Van Oosten H, Kumar L, Janssen JAM (2004)
Mapping coastal vegetation using an expert system and hyperspectral imagery. Photogramm
Eng Remote Sens 70(7):703–715
Skidmore AK, Turner BJ, Brinkhof W, Knowles E (1997) Performance of a neural network:
mapping forests using GIS and remotely sensed data. Photogramm Eng Remote Sens
63(5):501–514
Su L (2009) Optimizing support vector machine learning for semi-arid vegetation mapping by
using clustering analysis. ISPRS J Photogramm Remote Sens 64(4):407–413
Su L, Huang X (2009) Support vector machine (svm) classification: comparison of linkage
techniques using a clustering-based method for training data selection. GISci Remote Sens
46(4):411–423
Turner BL, Meyer WB (eds) (1994) Changes in land use and land cover: a global perspective.
Cambridge University Press, Cambridge, UK
Vapnik VN (1995) The nature of statistical learning theory. Springer, New York
Yang X (2002) Satellite monitoring of urban spatial growth in the Atlanta metropolitan region.
Photogramm Eng Remote Sens 68(7):725–734
Yang X (2011) Parameterizing support vector machines for land cover classification. Photogramm
Eng Remote Sens 77(1):27–37
Yang X, Lo CP (2002) Using a time series of normalized satellite imagery to detect land use/cover
change in the Atlanta, Georgia metropolitan area. Int J Remote Sens 23(9):1775–1798
Zhou L, Yang X (2011) An assessment of internal neural network parameters affecting image
classification accuracy. Photogramm Eng Remote Sens 77(12):1233–1240
13 Support Vector Machines for Land Cover Mapping from Remote Sensor Imagery
279
performance of pixel window algorithms in the classification of habitats using VHSR imagery.
ISPRS J Photogramm Remote Sens 60(4):225–238
Keuchel J, Naumann S, Heiler M, Siegmund A (2003) Automatic land cover analysis for Tenerife
by supervised classification using remotely sensed data. Remote Sens Environ 86(4):530–541
Liu T, Yang X (2013) Mapping urban vegetation using layered classification and multiple
endmember spectral mixture analysis. Remote Sens Environ 133:251–264
Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J
Photogramm Remote Sens 66(3):247–259
Pal M, Mather PM (2005) Support vector machines for classification in remote sensing. Int J
Remote Sens 26(5):1007–1011
Plaza A, Benediktsson JA, Boardman JW, Brazile J, Bruzzone L, Camps-valls G, Chanussot J,
Fauvel M, Gamba P, Gualtieri A, Marconcini M, Tilton JC, Trianni G (2009) Recent advances
in techniques for hyperspectral image processing. Remote Sens Environ 113(1):110–122
Richards JA, Landgrebe DA, Swain PH (1982) A means for utilizing ancillary information in
multispectral classifications. Remote Sens Environ 12(6):463–477
Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP (2012) An
assessment of the effectiveness of a random forest classifier for land-cover classification.
ISPRS J Photogramm Remote Sens 67:93–104
Schmidt KS, Skidmore AK, Kloosterman EH, Van Oosten H, Kumar L, Janssen JAM (2004)
Mapping coastal vegetation using an expert system and hyperspectral imagery. Photogramm
Eng Remote Sens 70(7):703–715
Skidmore AK, Turner BJ, Brinkhof W, Knowles E (1997) Performance of a neural network:
mapping forests using GIS and remotely sensed data. Photogramm Eng Remote Sens
63(5):501–514
Su L (2009) Optimizing support vector machine learning for semi-arid vegetation mapping by
using clustering analysis. ISPRS J Photogramm Remote Sens 64(4):407–413
Su L, Huang X (2009) Support vector machine (svm) classification: comparison of linkage
techniques using a clustering-based method for training data selection. GISci Remote Sens
46(4):411–423
Turner BL, Meyer WB (eds) (1994) Changes in land use and land cover: a global perspective.
Cambridge University Press, Cambridge, UK
Vapnik VN (1995) The nature of statistical learning theory. Springer, New York
Yang X (2002) Satellite monitoring of urban spatial growth in the Atlanta metropolitan region.
Photogramm Eng Remote Sens 68(7):725–734
Yang X (2011) Parameterizing support vector machines for land cover classification. Photogramm
Eng Remote Sens 77(1):27–37
Yang X, Lo CP (2002) Using a time series of normalized satellite imagery to detect land use/cover
change in the Atlanta, Georgia metropolitan area. Int J Remote Sens 23(9):1775–1798
Zhou L, Yang X (2011) An assessment of internal neural network parameters affecting image
classification accuracy. Photogramm Eng Remote Sens 77(12):1233–1240
13 Support Vector Machines for Land Cover Mapping from Remote Sensor Imagery
279