A Comparative Assessment of Multi-view fusion learning for Crop Classification

TL;DR

This paper compares various multi-view fusion strategies for crop classification using remote sensing data, showing that no single method is best universally but performance varies by region and dataset.

Contribution

It provides a comprehensive assessment of multiple fusion techniques for crop classification, highlighting their relative strengths and proposing criteria for selecting appropriate methods.

Findings

Fusion methods outperform individual data source models.

No single fusion method is best across all datasets.

Performance depends on the specific region and dataset.

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.