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Analysis of MultiSensor VHR image Time Series (MS-TS)
The use of remote sensing in the analysis and evaluation of environmental degeneration processes has become a valuable tool which relevance increased in conjunction with the use of digital image processing techniques. The improvement in acquisition sensor technology as well as in the data processing algorithm allowed an accurate and automatic identification and extraction of characteristics for the understanding of the environmental changes occurring due to natural and anthropic interactions.
The technological evolution resulted in the availability of multitemporal images with very high spatial resolution (VHR) acquired by both passive (e.g., QuickBird, GeoEye, World-View, Ikonos, Pleiades) and active sensors (e.g. TerraSAR-X, CosmoSkyMED). However it is difficult to build time series of images over a specific area of interest with a proper time sampling and characteristics (e.g., low cloud cover, similar acquisition angle) when considering one single acquisition sensor. This is mainly due to the revisit of satellites, competing orders, and the weather conditions (in the case of passive sensors). However, since a considerable number of satellites have been lunched to the space in the last decades, it is possible to construct time series by considering images acquired by different sensors and therefore mitigate the above limitations. In other words the definition of multisensor time series increases the probability to have frequent images and decreases the drawbacks due to weather conditions when dealing with passive sensor images.
This project is developed by the Remote Sensing for Digital Earth (RSDE) unit from Fondazione Bruno Kessler – IRST in collaboration with the Remote Sensing Laboratory (RSLab) from Università degli Studi di Trento and with the DigitalGLobe Foundation who has provided some of the images used in this project.