Crop-type classification has been attracting a lot of attention in recent years. In particular since the launch of the Sentinel-2 (S2) satellite which combines a large amount of spectral and spatial information, compared to previous satellite generations. In the literature, several methods exist that perform crop classification in time series, but most of them: i) work at pixel level; ii) perform single-data analysis; and/or iii) consider a single feature. This results in low performance of state-of-the-art methods. This paper presents an approach that works at object-level and exploits both spatial and temporal information coded in NDVI time series and phenological parameters and takes advantage of a semi-supervised paradigm by combining a new hierarchical correlation clustering with an artificial neural network. The effectiveness of the proposed approach was corroborated over an intensive cultivated area located in Barrax, Spain. Crop-type classification was compared to state-of-the-art methods.

A Semi-Supervised Crop-Type Classification Based on Sentinel-2 NDVI Satellite Image Time Series And Phenological Parameters / Solano-Correa, Yady Tatiana; Bovolo, Francesca; Bruzzone, Lorenzo. - CD-ROM. - (2019), pp. 457-460. (Intervento presentato al convegno IGARSS 2019 tenutosi a Yokohama, Japan nel 29th July-2nd August 2019) [10.1109/IGARSS.2019.8897922].

A Semi-Supervised Crop-Type Classification Based on Sentinel-2 NDVI Satellite Image Time Series And Phenological Parameters

Solano-Correa, Yady Tatiana;Bovolo, Francesca;Bruzzone, Lorenzo
2019-01-01

Abstract

Crop-type classification has been attracting a lot of attention in recent years. In particular since the launch of the Sentinel-2 (S2) satellite which combines a large amount of spectral and spatial information, compared to previous satellite generations. In the literature, several methods exist that perform crop classification in time series, but most of them: i) work at pixel level; ii) perform single-data analysis; and/or iii) consider a single feature. This results in low performance of state-of-the-art methods. This paper presents an approach that works at object-level and exploits both spatial and temporal information coded in NDVI time series and phenological parameters and takes advantage of a semi-supervised paradigm by combining a new hierarchical correlation clustering with an artificial neural network. The effectiveness of the proposed approach was corroborated over an intensive cultivated area located in Barrax, Spain. Crop-type classification was compared to state-of-the-art methods.
2019
2019 IEEE International Geoscience and Remote Sensing Symposium Proceedings
Piscataway, NJ
IEEE
978-1-5386-9154-0
Solano-Correa, Yady Tatiana; Bovolo, Francesca; Bruzzone, Lorenzo
A Semi-Supervised Crop-Type Classification Based on Sentinel-2 NDVI Satellite Image Time Series And Phenological Parameters / Solano-Correa, Yady Tatiana; Bovolo, Francesca; Bruzzone, Lorenzo. - CD-ROM. - (2019), pp. 457-460. (Intervento presentato al convegno IGARSS 2019 tenutosi a Yokohama, Japan nel 29th July-2nd August 2019) [10.1109/IGARSS.2019.8897922].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/247218
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