In several human activities, such as agriculture and forest management, the monitoring of radiometric surface temperature is key. In particular both high spatial resolution and high acquisition rate are desirable but, due to the hardware limitations, these two characteristics are not met by the same sensor. The fusion of remotely sensed data acquired by sensors with different spatial and temporal resolution is a profitable choice to face this issue. When the real-time requirement is relaxed, the data sequence can be processed as a whole, allowing to improve the final result. Within this framework, we propose a novel batch sharpening strategy, relying on interpolation, data fusion and Bayesian smoothing techniques, and we assess its effectiveness on SEVIRI and MODIS thermal data.
Addesso, P., Longo, M., Restaino, R., Vivone, G., Maltese, A. (2014). An interpolation-based data fusion scheme for enhancing the resolution of thermal image sequences. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp.4926-4929). NY : Institute of Electrical and Electronics Engineers Inc. [10.1109/IGARSS.2014.6947600].
An interpolation-based data fusion scheme for enhancing the resolution of thermal image sequences
MALTESE, Antonino
2014-01-01
Abstract
In several human activities, such as agriculture and forest management, the monitoring of radiometric surface temperature is key. In particular both high spatial resolution and high acquisition rate are desirable but, due to the hardware limitations, these two characteristics are not met by the same sensor. The fusion of remotely sensed data acquired by sensors with different spatial and temporal resolution is a profitable choice to face this issue. When the real-time requirement is relaxed, the data sequence can be processed as a whole, allowing to improve the final result. Within this framework, we propose a novel batch sharpening strategy, relying on interpolation, data fusion and Bayesian smoothing techniques, and we assess its effectiveness on SEVIRI and MODIS thermal data.
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