References

Bai, J., S. Xiang and C. Pan, 2013. A graph-based classification method for hyperspectral images. IEEE. Trans. Geosci. Remote Sens., 51: 803-817.
CrossRef  |  Direct Link  |  

Barbin, D., G. Elmasry, D.W. Sun and P. Allen, 2012. Near-infrared hyperspectral imaging for grading and classification of pork. Meat Sci., 90: 259-268.
CrossRef  |  PubMed  |  Direct Link  |  

Bazi, Y., N. Alajlan, F. Melgani, H. AlHichri and S. Malek et al., 2014. Differential evolution extreme learning machine for the classification of hyperspectral images. IEEE. Geosci. Remote Sens. Lett., 11: 1066-1070.
CrossRef  |  Direct Link  |  

Ganesh, B.P., S. Aravindan, S. Raja and A. Thirunavukkarasu, 2013. Hyperspectral satellite data (Hyperion) preprocessing-a case study on banded magnetite quartzite in Godumalai Hill, Salem, Tamil Nadu and India. Arab. J. Geosci., 6: 3249-3256.
CrossRef  |  Direct Link  |  

Gao, Y., R. Ji, P. Cui, Q. Dai and G. Hua, 2014. Hyperspectral image classification through bilayer graph-based learning. IEEE. Trans. Image Process., 23: 2769-2778.
CrossRef  |  PubMed  |  Direct Link  |  

Ghamisi, P., M.S. Couceiro, F.M. Martins and B.J. Atli, 2014. Multilevel image segmentation based on fractional-order Darwinian particle swarm optimization. IEEE Geosci. Remote Sens., 52: 2382-2394.
CrossRef  |  Direct Link  |  

Ji, R., Y. Gao, R. Hong, Q. Liu and D. Tao et al., 2014. Spectral-spatial constraint hyperspectral image classification. IEEE. Trans. Geosci. Remote Sens., 52: 1811-1824.
CrossRef  |  Direct Link  |  

Johnson, B.A. and K. Iizuka, 2016. Integrating openstreetmap crowdsourced data and landsat time-series imagery for rapid land use/land cover (lulc) mapping: Case study of the laguna de bay area of the philippines. Appl. Geography, 67: 140-149.
CrossRef  |  Direct Link  |  

Li, J., P.R. Marpu, A. Plaza, J.M.B. Dias and J.A. Benediktsson, 2013. Generalized composite kernel framework for hyperspectral image classification. IEEE. Trans. Geosci. Remote Sens., 51: 4816-4829.

Merentitis, A., C. Debes and R. Heremans, 2014. Ensemble learning in hyperspectral image classification: toward selecting a favorable bias-variance tradeoff. IEEE. J. Sel. Top. Appl. Earth Obs. Remote Sens., 7: 1089-1102.

Moreno, R., F. Corona, A. Lendasse, M. Grana and L.S. Galvao, 2014. Extreme learning machines for soybean classification in remote sensing hyperspectral images. Neurocomput., 128: 207-216.
CrossRef  |  Direct Link  |  

Nia, M.S., D.Z. Wang, S.A. Bohlman, P. Gader, S.J. Graves and M. Petrovic, 2015. Impact of atmospheric correction and image filtering on hyperspectral classification of tree species using support vector machine. J. Appl. Remote Sens., 9: 095990-095990.
CrossRef  |  Direct Link  |  

Tochon, G., J.B. Feret, S. Valero, R.E. Martin and D.E. Knapp et al., 2015. On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images. Remote Sens. Environ., 159: 318-331.
CrossRef  |  Direct Link  |  

Valls, G.C., D. Tuia, L. Bruzzone and J.A. Benediktsson, 2014. Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE. Signal Process. Mag., 31: 45-54.
CrossRef  |  Direct Link  |  

Villa, A., J. Chanussot, J.A. Benediktsson, C. Jutten and R. Dambreville, 2013. Unsupervised methods for the classification of hyperspectral images with low spatial resolution. Pattern Recognit., 46: 1556-1568.
CrossRef  |  Direct Link  |  

Wang, Z., N.M. Nasrabadi and T.S. Huang, 2014. Spatial-spectral classification of hyperspectral images using discriminative dictionary designed by learning vector quantization. IEEE. Trans. Geosci. Remote Sens., 52: 4808-4822.
CrossRef  |  Direct Link  |  

Zhu, Z. and C.E. Woodcock, 2014. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change. Remote Sens. Environ., 152: 217-234.
CrossRef  |  Direct Link  |