Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover Classification
This work provides a practical guide for researchers in remote sensing to enhance AI4EO applications through optimized data fusion, though it is incremental as it builds on existing CNN methods.
The study tackled the problem of selecting the best data fusion framework for land-cover classification by analyzing four CNN-based paradigms applied to SAR and multispectral Sentinel data, resulting in a systematic procedure that improves classification outcomes.
Data fusion is a well-known technique, becoming more and more popular in the Artificial Intelligence for Earth Observation (AI4EO) domain mainly due to its ability of reinforcing AI4EO applications by combining multiple data sources and thus bringing better results. On the other hand, like other methods for satellite data analysis, data fusion itself is also benefiting and evolving thanks to the integration of Artificial Intelligence (AI). In this letter, four data fusion paradigms, based on Convolutional Neural Networks (CNNs), are analyzed and implemented. The goals are to provide a systematic procedure for choosing the best data fusion framework, resulting in the best classification results, once the basic structure for the CNN has been defined, and to help interested researchers in their work when data fusion applied to remote sensing is involved. The procedure has been validated for land-cover classification but it can be transferred to other cases.