Loyola, Diego and Coldewey-Egbers, Melanie (2012) Multi-sensor data merging with stacked neural networks for the creation of satellite long-term climate data records. EURASIP Journal on Advances in Signal Processing, 2012:91, pp. 1-10. DOI: 10.1186/1687-6180-2012-91.
Full text not available from this repository.
Official URL: http://asp.eurasipjournals.com/content/2012/1/91
This article presents a novel artificial neural network technique for merging multi-sensor satellite data. Stacked neural networks (NNs) are used to learn the temporal and spatial drifts between data from different satellite sensors. The resulting NNs are then used to sequentially adjust the satellite data for the creation of a global homogeneous long-term climate data record. The proposed technique has successfully been applied to the merging of ozone data from three European satellite sensors covering together a time period of more than 16 years. The resulting long-term ozone data record has an excellent long-term stability of 0.2 ± 0.2% per decade and can therefore be used for ozone and climate studies.
|Title:||Multi-sensor data merging with stacked neural networks for the creation of satellite long-term climate data records|
|Journal or Publication Title:||EURASIP Journal on Advances in Signal Processing|
|In Open Access:||Yes|
|In ISI Web of Science:||No|
|Page Range:||pp. 1-10|
|Keywords:||stacked neural networks; multi-sensor data merging; satellite ozone|
|HGF - Research field:||Aeronautics, Space and Transport (old)|
|HGF - Program:||Space (old)|
|HGF - Program Themes:||W EO - Erdbeobachtung|
|DLR - Research area:||Space|
|DLR - Program:||W EO - Erdbeobachtung|
|DLR - Research theme (Project):||W - Vorhaben Ozon-SAF (old)|
|Institutes and Institutions:||Remote Sensing Technology Institute|
|Deposited By:||Diego Loyola|
|Deposited On:||26 Jun 2012 06:58|
|Last Modified:||04 Apr 2013 16:37|
Repository Staff Only: item control page