Polygons do not necessarily correspond to spectrally homogeneous areas on the ground (i.e., parcel). Moreover, the vector polygon boundaries do not match perfectly the pixel grid. In this framework, there are a one-to-many and a many-to-one relations between polygons and pixels labels. In such complex and ill-posed problem, it is necessary to define a strategy for identifying pure pixels associated to a valid label (i.e., belonging to the predominant semantic class of the polygon).
Besides the spatial and pixel decomposition, the map legends usually present semantic-classes (e.g., the “crop” label is assigned to all pixels representing different cultivations, i.e., natural classes). This leads to a semantic gap between the map and the set of natural classes that can be discriminated by the spectral information provided by the RS images. To extract an informative and representative set of labeled samples from the thematic map, for each LC class it is necessary to perform a semantic decomposition of the legend map.
The partnership of the MTA project supported by ESA is led by the Remote Sensing Laboratory (RSLab) at the Department of Information Engineering and Computer Science of the University of Trento (UoT) and involves the Remote Sensing for Digital Earth Unit at the Fondazione Bruno Kessler (FBK) research center.
The RSLab coordinated by Prof. Lorenzo Bruzzone has a huge know-how on the definition, implementation and application of techniques and algorithms for the analysis of multitemporal images. RSLab has a widely recognized leadership in multitemporal data analysis and processing of time series in terms of change detection, and analysis of long time series and updating of land-cover maps. This is demonstrated by the quantity of RSLab publications on these specific topics (and their quality proven by citations), by the projects/applications managed by the group, and by the many initiative related to the temporal variable in remote sensing RSLab has managed. RSLab is the Prime Contractor of this proposal and has the scientific responsibility of the analysis of the state-of-the art, of selecting/defining the most effective methods and algorithms to be implemented in order to satisfy the requirements of the call, and of defining the road map for future activities. RSLab will then contribute to the implementation and to the validation of the algorithms.
The RSDE is an application oriented research group developed within the internationally well-known FBK research center. This Unit is built around the large know of the leader (Dr. Francesca Bovolo) in RS, who is a top expert in the field of multitemporal data analysis. RSDE in the project will lead the implementation, testing and validation of the developed tools on S-2 multitemporal datasets taking into account the large expertise and computation infrastructure the center has on the processing of large quantity of data. Moreover RSDE will contribute to the selection and implementation of the algorithms and to the definition of the road map.