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

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.



Titolo: VHR time-series generation by prediction and fusion of multi-sensor images
Autori: Solano-Correa, Yady Tatiana; Bovolo, Francesca; Bruzzone, Lorenzo
Autori Unitn: 
Solano Correa, Yady Tatiana
Bovolo, Francesca
Bruzzone, Lorenzo
Titolo del volume contenente il saggio: 2015 IEEE InternationalGeoscience and Remote Sensing Symposium (IGARSS)
Luogo di edizione: Milan, Italy
Casa editrice: IEEE
Anno di pubblicazione: 2015
ISBN: 978-1-4799-7929-5
978-1-4799-7929-5
Handle: http://hdl.handle.net/11572/117332
Appare nelle tipologie:04.1 Saggio in atti di convegno (Paper in proceedings)

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