Since optical remote sensing images are useless in cloudy conditions, a possible alternative is to resort to synthetic aperture radar (SAR) images. However, many conventional techniques for Earth monitoring applications require specific spectral features which are defined only for multispectral data. For this reason, in this work we propose to estimate missing spectral features through data fusion and deep learning, exploiting both temporal and cross-sensor dependencies on Sentinel-1 and Sentinel-2 time-series. The proposed approach, validated focusing on the estimation of the normalized difference vegetation index (NDVI), shows very interesting results with a large performance gain over the linear regression approach according to several accuracy indicators.

Estimating the NDVI from SAR by convolutional neural networks / Mazza, A.; Gargiulo, M.; Gaetano, R.; Scarpa, G.. - (2018), pp. 1954-1957. (Intervento presentato al convegno 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 tenutosi a Valencia (Spain) nel 2018) [10.1109/IGARSS.2018.8519459].

Estimating the NDVI from SAR by convolutional neural networks

Mazza A.
Primo
;
Gargiulo M.
Secondo
;
Gaetano R.
Ultimo
;
Scarpa G.
Penultimo
2018

Abstract

Since optical remote sensing images are useless in cloudy conditions, a possible alternative is to resort to synthetic aperture radar (SAR) images. However, many conventional techniques for Earth monitoring applications require specific spectral features which are defined only for multispectral data. For this reason, in this work we propose to estimate missing spectral features through data fusion and deep learning, exploiting both temporal and cross-sensor dependencies on Sentinel-1 and Sentinel-2 time-series. The proposed approach, validated focusing on the estimation of the normalized difference vegetation index (NDVI), shows very interesting results with a large performance gain over the linear regression approach according to several accuracy indicators.
2018
978-1-5386-7150-4
Estimating the NDVI from SAR by convolutional neural networks / Mazza, A.; Gargiulo, M.; Gaetano, R.; Scarpa, G.. - (2018), pp. 1954-1957. (Intervento presentato al convegno 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 tenutosi a Valencia (Spain) nel 2018) [10.1109/IGARSS.2018.8519459].
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: https://hdl.handle.net/11588/813986
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 22
  • ???jsp.display-item.citation.isi??? 18
social impact