Publication
A Comparative Assessment of Multi-view Fusion Learning for Crop Classification
Francisco Mena; Diego Arenas; Marlon Nuske; Andreas Dengel
In: 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE International Geoscience and Remote Sensing Symposium (IGARSS-2023), WE1.R2: Multisensor Fusion, July 16-21, Pasadena, California, USA, IEEE, 10/2023.
Abstract
With a rapidly increasing amount and diversity of remote sensing (RS) data sources, there is a strong need for multi-view learning modeling. This is a complex task when considering the differences in resolution, magnitude, and noise of RS data. The typical approach for merging multiple RS sources has been input-level fusion, but other - more advanced fusion strategies may outperform this traditional approach. This work assesses different fusion strategies for crop classification in the CropHarvest dataset. The fusion methods proposed in this work outperform models based on individual views and previous fusion methods. We do not find one single fusion method that consistently outperforms all other approaches. Instead, we present a comparison of multi-view fusion methods for three different datasets and show that, depending on the test region, different methods obtain the best performance. Despite this, we suggest a preliminary criterion
for the selection of fusion methods.