References
Alberti M (2008) Advances in urban ecology: integrating humans and ecological processes in
urban ecosystems. Springer, New York
Bazi Y, Melgani F (2006) Toward an optimal SVM classification system for hyperspectral remote
sensing images. Geosci Remote Sens IEEE Trans 44(11):3374–3385
Camps-Valls G, Gomez-Chova L, Mu~ noz-Mari J, Vila-Frances J, Calpe-Maravilla J (2006)
Composite kernels for hyperspectral image classification. IEEE Geosci Remote Sens Lett
3(1):93–97
Camps-Valls G, Bandos T, Zhou D (2007) Semi-supervised graph-based hyperspectral image
classification. IEEE Trans Geosci Remote Sens 45(10):3044–3054
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed
data. Remote Sens Environ 37(1):35–46
Del Frate F, Pacifici F, Schiavon G, Solimini C (2007) Use of neural networks for automatic
classification from high-resolution images. IEEE Trans Geosci Remote Sens 45(4):800–809
Demir B, Ertu ¨rk S (2009) Clustering based extraction of border training patterns for accurate SVM
classification of hyperspectral images. IEEE Geosci Remote Sens Lett 6(4):840–844
Dixon B, Candade N (2008) Multispectral landuse classification using neural networks and
support vector machines: one or the other, or both? Int J Remote Sens 29(4):1185–1206
Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York
Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, Chapin FS, Coe MT, Daily
GC, Gibbs HK, Helkowski JH, Holloway T, Howard TEA, Kucharik CJ, Monfreda C, Patz JA,
Prentice IC, Ramankutty N, Snyder PK (2005) Global consequences of land use. Science
309:570–574
Foody GM (2008) RVM-based multi-class classification of remotely sensed data. Int J Remote
Sens 29(6):1817–1823
Foody GM, Arora MK (1997) An evaluation of some factors affecting the accuracy of classification by an artificial neural network. Int J Remote Sens 18:799–810
Foody GM, Mathur A (2004a) Toward intelligent training of supervised image classifications:
directing training data acquisition for SVM classification. Remote Sens Environ 93(1–2):107–117
Foody GM, Mathur A (2004b) A relative evaluation of multiclass image classification by support
vector machines. IEEE Trans Geosci Remote Sens 42(6):1335–1343
Foody GM, Mathur A (2006) The use of small training sets containing mixed pixels for accurate
hard image classification: training on mixed spectral responses for classification by a SVM.
Remote Sens Environ 103(2):179–189
Haykin S (1999) Neural networks: a comprehensive foundations, 2nd edn. Prentice Hall, Upper
Saddle River
Heikkinen V, Tokola T, Parkkinen J, Korpela I, Jaaskelainen T (2010) Simulated multispectral
imagery for tree species classification using support vector machines. IEEE Trans Geosci
Remote Sens 48(3):1355–1364
Hoffer RM (1978) Biological and physical considerations in applying computer aided analysis
techniques to the remote sensor data. In: Swain PH, Davis SM (eds) Remote sensing: the
quantitative approach. McGraw-Hill, New York, pp 227–289
Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. Neural
Netw IEEE Trans 13(2):415–425
Huang C, Davis LS, Townshend JRG (2002) An assessment of support vector machines for land
cover classification. Int J Remote Sens 23(4):725–749
Hughes GF (1968) On the mean accuracy of statistical pattern recognizers. IEEE Trans Inf Theory
14:55–63
Jensen JR (2005) Introductory digital image processing: a remote sensing perspective, 5th edn.
Prentice Hall, Upper Saddle River
Kavzoglu T, Colkesen I (2009) A kernel functions analysis for support vector machines for land
cover classification. Int J Appl Earth Obs Geoinfr 11(5):352–359
278
D. Shi and X. Yang
Alberti M (2008) Advances in urban ecology: integrating humans and ecological processes in
urban ecosystems. Springer, New York
Bazi Y, Melgani F (2006) Toward an optimal SVM classification system for hyperspectral remote
sensing images. Geosci Remote Sens IEEE Trans 44(11):3374–3385
Camps-Valls G, Gomez-Chova L, Mu~ noz-Mari J, Vila-Frances J, Calpe-Maravilla J (2006)
Composite kernels for hyperspectral image classification. IEEE Geosci Remote Sens Lett
3(1):93–97
Camps-Valls G, Bandos T, Zhou D (2007) Semi-supervised graph-based hyperspectral image
classification. IEEE Trans Geosci Remote Sens 45(10):3044–3054
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed
data. Remote Sens Environ 37(1):35–46
Del Frate F, Pacifici F, Schiavon G, Solimini C (2007) Use of neural networks for automatic
classification from high-resolution images. IEEE Trans Geosci Remote Sens 45(4):800–809
Demir B, Ertu ¨rk S (2009) Clustering based extraction of border training patterns for accurate SVM
classification of hyperspectral images. IEEE Geosci Remote Sens Lett 6(4):840–844
Dixon B, Candade N (2008) Multispectral landuse classification using neural networks and
support vector machines: one or the other, or both? Int J Remote Sens 29(4):1185–1206
Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York
Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, Chapin FS, Coe MT, Daily
GC, Gibbs HK, Helkowski JH, Holloway T, Howard TEA, Kucharik CJ, Monfreda C, Patz JA,
Prentice IC, Ramankutty N, Snyder PK (2005) Global consequences of land use. Science
309:570–574
Foody GM (2008) RVM-based multi-class classification of remotely sensed data. Int J Remote
Sens 29(6):1817–1823
Foody GM, Arora MK (1997) An evaluation of some factors affecting the accuracy of classification by an artificial neural network. Int J Remote Sens 18:799–810
Foody GM, Mathur A (2004a) Toward intelligent training of supervised image classifications:
directing training data acquisition for SVM classification. Remote Sens Environ 93(1–2):107–117
Foody GM, Mathur A (2004b) A relative evaluation of multiclass image classification by support
vector machines. IEEE Trans Geosci Remote Sens 42(6):1335–1343
Foody GM, Mathur A (2006) The use of small training sets containing mixed pixels for accurate
hard image classification: training on mixed spectral responses for classification by a SVM.
Remote Sens Environ 103(2):179–189
Haykin S (1999) Neural networks: a comprehensive foundations, 2nd edn. Prentice Hall, Upper
Saddle River
Heikkinen V, Tokola T, Parkkinen J, Korpela I, Jaaskelainen T (2010) Simulated multispectral
imagery for tree species classification using support vector machines. IEEE Trans Geosci
Remote Sens 48(3):1355–1364
Hoffer RM (1978) Biological and physical considerations in applying computer aided analysis
techniques to the remote sensor data. In: Swain PH, Davis SM (eds) Remote sensing: the
quantitative approach. McGraw-Hill, New York, pp 227–289
Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. Neural
Netw IEEE Trans 13(2):415–425
Huang C, Davis LS, Townshend JRG (2002) An assessment of support vector machines for land
cover classification. Int J Remote Sens 23(4):725–749
Hughes GF (1968) On the mean accuracy of statistical pattern recognizers. IEEE Trans Inf Theory
14:55–63
Jensen JR (2005) Introductory digital image processing: a remote sensing perspective, 5th edn.
Prentice Hall, Upper Saddle River
Kavzoglu T, Colkesen I (2009) A kernel functions analysis for support vector machines for land
cover classification. Int J Appl Earth Obs Geoinfr 11(5):352–359
278
D. Shi and X. Yang