A Comparative Assessment of Multi-view fusion learning for Crop
  Classification
Francisco Mena Diego Arenas Marlon Nuske Andreas Dengel
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.