Optimizing the ground sample collection with cost-sensitive active learning for tree species classification using hyperspectral images

C. Persello; M. Dalponte; T. Gobakken; E. Naesset

doi:10.1109/IGARSS.2013.6723224

Optimizing the ground sample collection with cost-sensitive active learning for tree species classification using hyperspectral images

C. Persello, M. Dalponte, T. Gobakken, E. Naesset

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Original language	English
Title of host publication	Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2013), 21-26 July 2013, Melbourne, Australia.
Place of Publication	Washington D.C., USA
Publisher	IEEE
Pages	2091-2094
ISBN (Print)	978-1-4799-1114-1
DOIs	https://doi.org/10.1109/IGARSS.2013.6723224
Publication status	Published - 2013

Keywords

ADLIB-ART-4777

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IGARSS.2013.6723224

Cite this

Persello, C., Dalponte, M., Gobakken, T., & Naesset, E. (2013). Optimizing the ground sample collection with cost-sensitive active learning for tree species classification using hyperspectral images. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2013), 21-26 July 2013, Melbourne, Australia. (pp. 2091-2094). IEEE. https://doi.org/10.1109/IGARSS.2013.6723224

Persello, C. ; Dalponte, M. ; Gobakken, T. et al. / Optimizing the ground sample collection with cost-sensitive active learning for tree species classification using hyperspectral images. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2013), 21-26 July 2013, Melbourne, Australia. . Washington D.C., USA : IEEE, 2013. pp. 2091-2094

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Persello, C, Dalponte, M, Gobakken, T & Naesset, E 2013, Optimizing the ground sample collection with cost-sensitive active learning for tree species classification using hyperspectral images. in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2013), 21-26 July 2013, Melbourne, Australia. . IEEE, Washington D.C., USA, pp. 2091-2094. https://doi.org/10.1109/IGARSS.2013.6723224

Optimizing the ground sample collection with cost-sensitive active learning for tree species classification using hyperspectral images. / Persello, C.; Dalponte, M.; Gobakken, T. et al.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2013), 21-26 July 2013, Melbourne, Australia. . Washington D.C., USA: IEEE, 2013. p. 2091-2094.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - Optimizing the ground sample collection with cost-sensitive active learning for tree species classification using hyperspectral images

AU - Persello, C.

AU - Dalponte, M.

AU - Gobakken, T.

AU - Naesset, E.

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M3 - Chapter

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EP - 2094

BT - Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2013), 21-26 July 2013, Melbourne, Australia.

PB - IEEE

CY - Washington D.C., USA

ER -

Persello C, Dalponte M, Gobakken T, Naesset E. Optimizing the ground sample collection with cost-sensitive active learning for tree species classification using hyperspectral images. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2013), 21-26 July 2013, Melbourne, Australia. . Washington D.C., USA: IEEE. 2013. p. 2091-2094 doi: 10.1109/IGARSS.2013.6723224

Optimizing the ground sample collection with cost-sensitive active learning for tree species classification using hyperspectral images

Keywords

UN SDGs

Access to Document

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