TY - GEN
T1 - VHR time-series generation by prediction and fusion of multi-sensor images
AU - Correa, Yady Tatiana Solano
AU - Bovolo, Francesca
AU - Bruzzone, Lorenzo
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/10
Y1 - 2015/11/10
N2 - 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.
AB - 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.
KW - Change Detection
KW - Multi-Sensor fusion
KW - Prediction
KW - Radiometric Normalization
KW - VHR Time-Series
UR - https://www.scopus.com/pages/publications/84962595112
U2 - 10.1109/IGARSS.2015.7326523
DO - 10.1109/IGARSS.2015.7326523
M3 - Conference contribution
AN - SCOPUS:84962595112
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3298
EP - 3301
BT - 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Y2 - 26 July 2015 through 31 July 2015
ER -