Semi-supervised Hyperspectral Image Classification with Graphs
2006
Conference Paper
ei
This paper presents a semi-supervised graph-based method for the classification of hyperspectral images. The method is designed to exploit the spatial/contextual information in the images through composite kernels. The proposed method produces smoother classifications with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. Good accuracy in high dimensional spaces and low number of labeled samples (ill-posed situations) are produced as compared to standard inductive support vector machines.
Author(s): | Bandos, TV. and Zhou, D. and Camps-Valls, G. |
Book Title: | IGARSS 2006 |
Journal: | Proceedings of the IEEE International Conference on Geoscience and Remote Sensing (IGARSS 2006) |
Pages: | 3883-3886 |
Year: | 2006 |
Month: | August |
Day: | 0 |
Publisher: | IEEE Computer Society |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
DOI: | 10.1109/IGARSS.2006.996 |
Event Name: | IEEE International Conference on Geoscience and Remote Sensing |
Event Place: | Denver, CO, USA |
Address: | Los Alamitos, CA, USA |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @inproceedings{3955, title = {Semi-supervised Hyperspectral Image Classification with Graphs}, author = {Bandos, TV. and Zhou, D. and Camps-Valls, G.}, journal = {Proceedings of the IEEE International Conference on Geoscience and Remote Sensing (IGARSS 2006)}, booktitle = {IGARSS 2006}, pages = {3883-3886}, publisher = {IEEE Computer Society}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Los Alamitos, CA, USA}, month = aug, year = {2006}, doi = {10.1109/IGARSS.2006.996}, month_numeric = {8} } |