Learning spatial filters for multispectral image segmentation

Devis Tuia; Gustavo Camps-Valls; Remi Flamary; Alain Rakotomamonjy

doi:10.1109/MLSP.2010.5589202

Learning spatial filters for multispectral image segmentation

Devis Tuia
, Gustavo Camps-Valls
, Remi Flamary
, Alain Rakotomamonjy

University of Valencia
Normandie Université

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

Abstract

We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.

Original language	English
Title of host publication	Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010
Pages	41-46
Number of pages	6
DOIs	https://doi.org/10.1109/MLSP.2010.5589202
Publication status	Published - 24 Nov 2010
Externally published	Yes
Event	2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010 - Kittila, Finland Duration: 29 Aug 2010 → 1 Sept 2010

Publication series

Name	Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010

Conference

Conference	2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010
Country/Territory	Finland
City	Kittila
Period	29/08/10 → 1/09/10

Access to Document

10.1109/MLSP.2010.5589202

Cite this

Tuia, D., Camps-Valls, G., Flamary, R., & Rakotomamonjy, A. (2010). Learning spatial filters for multispectral image segmentation. In Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010 (pp. 41-46). Article 5589202 (Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010). https://doi.org/10.1109/MLSP.2010.5589202

@inproceedings{dc32ac3f1572406a8353201e8f586bb1,

title = "Learning spatial filters for multispectral image segmentation",

abstract = "We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.",

author = "Devis Tuia and Gustavo Camps-Valls and Remi Flamary and Alain Rakotomamonjy",

year = "2010",

month = nov,

day = "24",

doi = "10.1109/MLSP.2010.5589202",

language = "English",

isbn = "9781424478774",

series = "Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010",

pages = "41--46",

booktitle = "Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010",

note = "2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010 ; Conference date: 29-08-2010 Through 01-09-2010",

}

Tuia, D, Camps-Valls, G, Flamary, R & Rakotomamonjy, A 2010, Learning spatial filters for multispectral image segmentation. in Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010., 5589202, Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010, pp. 41-46, 2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010, Kittila, Finland, 29/08/10. https://doi.org/10.1109/MLSP.2010.5589202

Learning spatial filters for multispectral image segmentation. / Tuia, Devis; Camps-Valls, Gustavo; Flamary, Remi et al.
Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010. 2010. p. 41-46 5589202 (Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010).

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

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AB - We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.

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Tuia D, Camps-Valls G, Flamary R, Rakotomamonjy A. Learning spatial filters for multispectral image segmentation. In Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010. 2010. p. 41-46. 5589202. (Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010). doi: 10.1109/MLSP.2010.5589202

Learning spatial filters for multispectral image segmentation

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