In this paper a new approach from the combination of band ratioing function and MLP Neural Networks technique is proposed to differentiate between clouds and background in Landsat ETM+ and MSG SEVIRI data. First, in order to increase the contrast of the clouds and background, a band ratioing function is applied to each sub-image. Second, the sub-images are segmented by MLP Neural Networks technique. The proposed approach was tested on 40 Landsat ETM+ sub-images of Gulf of Mexico and on 40 MSG SEVIRI sub-images over Italy. The same parameters were used in all tests. For the overall dataset, the average accuracy of 89 % was obtained for Landsat ETM+ images and the average accuracy of 85 % was obtained for MSG SEVIRI images. Our experimental results demonstrate that the proposed approach is robust and effective.

Taravat, A., Peronaci, S., Sist, M., Del Frate, F., & Oppelt, N. (2015). The combination of band ratioing techniques and neural networks algorithms for MSG SEVIRI and Landsat ETM+ cloud masking. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp.2315-2318). Institute of Electrical and Electronics Engineers Inc. [10.1109/IGARSS.2015.7326271].

The combination of band ratioing techniques and neural networks algorithms for MSG SEVIRI and Landsat ETM+ cloud masking

DEL FRATE, FABIO;
2015

Abstract

In this paper a new approach from the combination of band ratioing function and MLP Neural Networks technique is proposed to differentiate between clouds and background in Landsat ETM+ and MSG SEVIRI data. First, in order to increase the contrast of the clouds and background, a band ratioing function is applied to each sub-image. Second, the sub-images are segmented by MLP Neural Networks technique. The proposed approach was tested on 40 Landsat ETM+ sub-images of Gulf of Mexico and on 40 MSG SEVIRI sub-images over Italy. The same parameters were used in all tests. For the overall dataset, the average accuracy of 89 % was obtained for Landsat ETM+ images and the average accuracy of 85 % was obtained for MSG SEVIRI images. Our experimental results demonstrate that the proposed approach is robust and effective.
IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
ita
2015
The Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (IEEE GRSS)
Rilevanza internazionale
contributo
Settore ING-INF/02 - Campi Elettromagnetici
English
Clouds; MSG SEVIRI; neural networks; earth and planetary sciences (all); computer science applications1707 computer vision and pattern recognition
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7326271&refinements=4291944822&refinements=4226718319&refinements=4226495737&searchWithin=%22First%20Name%22:Fabio&searchWithin=%22Last%20Name%22:Del%20Frate
Intervento a convegno
Taravat, A., Peronaci, S., Sist, M., Del Frate, F., & Oppelt, N. (2015). The combination of band ratioing techniques and neural networks algorithms for MSG SEVIRI and Landsat ETM+ cloud masking. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp.2315-2318). Institute of Electrical and Electronics Engineers Inc. [10.1109/IGARSS.2015.7326271].
Taravat, A; Peronaci, S; Sist, M; DEL FRATE, F; Oppelt, N
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2108/154027
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 3
social impact