TY - GEN
T1 - A stochastic minimum spanning forest approach for spectral-spatial classification of hyperspectral images
AU - Bernard, K.
AU - Tarabalka, Y.
AU - Angulo, J.
AU - Chanussot, J.
AU - Benediktsson, J. A.
PY - 2011
Y1 - 2011
N2 - A new method for supervised hyperspectral data classification is proposed. In particular, the notion of Stochastic Minimum Spanning Forests (MSFs) is introduced. For a given hyper-spectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of MSFs. The next step consists in building an MSF from each of the M marker maps. Finally, all the M realizations are aggregated with a maximum vote decision rule, resulting in a final classification map. The experimental results presented on an AVIRIS image of the vegetation area show that the proposed approach yields accurate classification maps, and thus is attractive for hyperspectral data analysis.
AB - A new method for supervised hyperspectral data classification is proposed. In particular, the notion of Stochastic Minimum Spanning Forests (MSFs) is introduced. For a given hyper-spectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of MSFs. The next step consists in building an MSF from each of the M marker maps. Finally, all the M realizations are aggregated with a maximum vote decision rule, resulting in a final classification map. The experimental results presented on an AVIRIS image of the vegetation area show that the proposed approach yields accurate classification maps, and thus is attractive for hyperspectral data analysis.
KW - Hyperspectral image
KW - classification
KW - minimum spanning forest
KW - multiple classifiers
KW - stochastic markers
UR - https://www.scopus.com/pages/publications/84856261531
U2 - 10.1109/ICIP.2011.6115664
DO - 10.1109/ICIP.2011.6115664
M3 - Conference contribution
SN - 9781457713033
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1265
EP - 1268
BT - ICIP 2011
T2 - 2011 18th IEEE International Conference on Image Processing, ICIP 2011
Y2 - 11 September 2011 through 14 September 2011
ER -