Bartels, M. and Wei, H., 2006a, Maximum likelihood classification of LIDAR
data incorporating multiple co-registered bands. In Proceedings of the IAPR
Workshop on Pattern Recognition in Remote Sensing, Sep., pp. 17–20.
Bartels, M. and Wei, H., 2006b, Rule-based improvement of maximum likelihood
classified LIDAR data fused with co-registered bands. In Proceedings of the Proc. Annual Conference of the Remote Sensing and Photogrammetry
Society, 1898, Sep., pp. 5–8.
Bartels, M., 2008, Segmentation and classification of terrain features in airborne
LIDAR data. PhD thesis, University of Reading, UK.
Besag, J., 1974, Spatial interaction and the statistical analysis of lattice systems.
Journal of the Royal Statistical Society, B36, pp. 192–236.
Bovolo, F. and Bruzzone, L., 2005, A context-sensitive technique based on
support vector machines for image classification. In Proceedings of the PReMI,
LNCS 3776, Oct., pp. 260–265.
Boykov, Y., Veksler, O. and Zabih, R., 1998, Markov random fields with efficient approximations. In Proceedings of the Conference Computer Vision
and Pattern Recognition, Aug., pp. 648–655.
Boykov, Y., Veksler, O. and Zabih, R., 2001, Fast approximate energy minimization
via graph cuts. IEEE Transaction on Pattern Analysis and Machine
Intelligence, 23, pp. 1222–1239.
Cao, Y., Wei, H., Bartels, M. and Zhao, H.J., 2008a, Comparison of rulebased
ML algorithm and MRF model in land-cover classification from LIDAR
and co-registered bands. In Proceedings of the Annual Conference of the Re-
mote Sensing and Photogrammetry Society, Sep.
Cao, Y., Wei, H. and Zhao, H.J., 2008b, Optimization algorithms in FMRF
model-based segmentation for LIDAR data and co-registered bands. In Pro-
ceedings of the IAPR Workshop on Pattern Recognition in Remote Sensing,
Dec., pp. 1–4.
Cicala, L., Poggi, G. and Scarpa, G., 2004, Supervised segmentation of remote-sensing multitemporal images based on the tree-structured Markov random field model. In Proceedings of the International Geoscience and Re-
mote Sensing Symposium, Sep., pp. 1569–1572.
Cristianini, N. and Shawe-Taylor, J., 2000, An introduction to support vector
machines and other kernel-based learning methods (Cambridge: Cambridge University Press).
Dubes, R.C. and Jain, A.K., 1990, MRF model-based algorithms for image segmentation.
In Proceedings of the International Conference on Pattern Recog-nition, 1, pp. 808–814.
Farag, A., Mohamed, R. and El-Baz, A., 2005, A unified framework for MAP
estimation in remote sensing image segmentation. IEEE Transaction on Geo-
science and Remote Sensing, 43, pp. 1617–1634.
Felzenszwalb, P. and Huttenlocher, D., 2006, Efficient belief propagation
for early vision. International Journal Computer Vision, 70, pp. 41–54.
Fornaciai, A., Bisson, M., Landi, P., Mazzarini, F. and Pareschi, M.T.,
2010, A LiDAR survey of Stromboli volcano (Italy): Digital elevation modelbased
geomorphology and intensity analysis. International Journal of Remote
Sensing, 31, pp. 391–405.
Gao, J., Gunn, S. and Harris, C., 2003, Mean field method for the support
vector machine regression. Neurocomputing, 50, pp. 391–405.
Geman, S. and Geman, D., 1984, Stochastic relaxation, Gibbs distributions and
Bayesian restoration of images. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 6, pp. 721–741.
Hegarat-Mascle, S.L., Bloch, I. and Vidal-Madjar, D., 1997, Application
of Dempster-Shafer evidence theory to unsupervised classification in multisource
remote sensing. IEEE Transaction on Geoscience and Remote Sensing,
35, pp. 1018–1031.
Huang, X., Zhang, L.P. and Gong, W., 2011, Information fusion of aerial images and LIDAR data in urban areas: vector-stacking, re-classification and
post-processing approaches. International Journal of Remote Sensing, 32, pp.
69–84.
Huising, E.J. and Gomes-Pereira, L.M., 1998, Errors and accuracy estimates
of laser data acquired by various laser scanning systems for topographic applications.
ISPRS Journal of Photogrammetry and Remote Sensing, 53, pp.
245–261.
Kolmogorov, V. and Rother, C., 2006, Comparison of energy minimization
algorithms for highly connected graphs. In Proceedings of the European Con-
ference Computer Vision, 1898, May, pp. 1–15.
Lin, H.T., Lin, C.J. and Weng, R.C., 2003, A note on Platt’s probabilistic outputs
for support vector machines. Technical report, Department of Computer
Science, National Taiwan University.
Lodha, S.K., Kreps, E.J., Helmbold, D.P. and Fitzpatrick, D., 2006, Aerial
LIDAR data classification using support vector machines (SVM). In Proceed-
ings of the International Symposium on 3D Data Processing, Visualization,
and Transmission, Jun., pp. 567–574.
Platt, J.C., 2000, Probabilistic outputs for support vector machines and comparison
to regularized likelihood methods. Advances in Large Margin Classi�ers,
MIT press.
Rottensteiner, F., Trinder, J., Clode, S. and Kbik, K., 2005, Using the Dempster-Shafer method for the fusion of LIDAR data and multi-spectral images for building detection. Information Fusion, 6, pp. 283–300.
Secord, J. and Zakhor, A., 2006, Tree detection in aerial LIDAR and image data. In Proceedings of the International Conference on Image Processing,Oct., pp. 2317–2320.
Shafer, G., 1976, A mathematical theory of evidence. Princeton University Press.
Sun, J., Zheng, N.N. and Shum, H.Y., 2003, Stereo matching using belief propagation.
IEEE Transaction on Pattern Analysis and Machine Intelligence, 25, pp. 787–800.
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V.,
Agarwala, A., Tappen, M. and Rother, C., 2008, A comparative study
of energy minimization methods for Markov random fields. IEEE Transaction
on Pattern Analysis and Machine Intelligence, 30, pp. 1068–1080.
Tappen, M. and Freeman, W., 2003, Comparison of graph cuts with belief
propagation for stereo, using identical MRF parameters. In Proceedings of the
International Conference Computer Vision, Oct., pp. 900–907.
Tarabalka, Y., Chanussot, J. and Benediktsson, J.A., 2010, Segmentation
and classification of hyperspectral images using minimum spanning forest
grown from automatically selected markers. IEEE Transaction System, Man,
and Cybernetics-part B, Cybernetics, 40, pp. 1267–1279.
Tso, B. and Mather, P.M., 2001, Classi�cation methods for remotely sensed
data (London: Taylor and Francis Ltd).
Wu, T.F., Lin, C.J. and Weng, R.C., 2004, Probability estimates for multiclass
classification by pairwise coupling. Journal of Machine Learning Research, 5,
pp. 975–1005.
Lists
Lists
