This paper presents a new paradigm for extracting information from large databases of remote sensing images. It aims at improving any task applied to image time series by exploiting properties related to their temporal cross-dependence. Images part of the same time series are casually related to each other. As a consequence, the results of the tasks are mutually entangled. The proposed paradigm exploits this property and validates the results of the tasks one to each other to improve the overall performance. The paradigm is general and has relevant implications in Big Data analysis because it is suitable to archives containing not only Earth Observed images but any time-varying quantity or feature. Preliminary results show that change detection accuracy improves after the evaluation of the conservative property within the image time series.
A New Paradigm for the Exploitation of the Semantic Content of Large Archives of Satellite Remote Sensing Images / Bruzzone, Lorenzo; Bertoluzza, Manuel; Bovolo, Francesca. - ELETTRONICO. - (2017), pp. 114-117. (Intervento presentato al convegno BiDS ’17 tenutosi a Toulouse, France nel 28-30 November 2017) [10.2760/383579].
A New Paradigm for the Exploitation of the Semantic Content of Large Archives of Satellite Remote Sensing Images
Lorenzo Bruzzone;Manuel Bertoluzza;Francesca Bovolo
2017-01-01
Abstract
This paper presents a new paradigm for extracting information from large databases of remote sensing images. It aims at improving any task applied to image time series by exploiting properties related to their temporal cross-dependence. Images part of the same time series are casually related to each other. As a consequence, the results of the tasks are mutually entangled. The proposed paradigm exploits this property and validates the results of the tasks one to each other to improve the overall performance. The paradigm is general and has relevant implications in Big Data analysis because it is suitable to archives containing not only Earth Observed images but any time-varying quantity or feature. Preliminary results show that change detection accuracy improves after the evaluation of the conservative property within the image time series.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione