The availability of multitemporal images acquired by several very high geometrical resolution (VHR) optical sensors makes it possible to build VHR image Time-Series (TS) with a temporal resolution better than the one achievable when considering a single sensor. However, such TS include images showing different characteristics from the geometrical, radiometrical and spectral viewpoint. Thus, there is a need of methods for building consistent VHR optical TS when using multispectral Multi-Sensor (MS) images. Here we focus on the spectral domain only, by designing a method to transform one image in an MS-TS into the spectral domain of another image in the same MS-TS, but acquired by a different sensor. To this end, a prediction-based approach relying on Artificial Neural Networks (ANN) is employed. In order to mitigate the impacts of possible changes occurred on the ground, the prediction model estimation is based on unchanged samples only. Experimental results obtained on VHR optical MS images confirm the effectiveness of the proposed approach.

VHR time-series generation by prediction and fusion of multi-sensor images

Solano Correa, Yady Tatiana;Bovolo, Francesca;Bruzzone, Lorenzo
2015-01-01

Abstract

The availability of multitemporal images acquired by several very high geometrical resolution (VHR) optical sensors makes it possible to build VHR image Time-Series (TS) with a temporal resolution better than the one achievable when considering a single sensor. However, such TS include images showing different characteristics from the geometrical, radiometrical and spectral viewpoint. Thus, there is a need of methods for building consistent VHR optical TS when using multispectral Multi-Sensor (MS) images. Here we focus on the spectral domain only, by designing a method to transform one image in an MS-TS into the spectral domain of another image in the same MS-TS, but acquired by a different sensor. To this end, a prediction-based approach relying on Artificial Neural Networks (ANN) is employed. In order to mitigate the impacts of possible changes occurred on the ground, the prediction model estimation is based on unchanged samples only. Experimental results obtained on VHR optical MS images confirm the effectiveness of the proposed approach.
2015
2015 IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS)
Milan, Italy
IEEE
978-1-4799-7929-5
978-1-4799-7929-5
Solano Correa, Yady Tatiana; Bovolo, Francesca; Bruzzone, Lorenzo
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/11572/117332
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 5
social impact