# Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover Classification

arXiv: https://arxiv.org/abs/2106.11056 · HTML: https://mlsanity.com/paper/2106.11056/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 18 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [Multi-temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation](https://mlsanity.com/paper/1807.09954/) (2018)
- [Automatic Dataset Builder for Machine Learning Applications to Satellite Imagery](https://mlsanity.com/paper/2008.01578/) (2020)

## Built on by (most cited first, 4 of 4)

- [Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications](https://mlsanity.com/paper/2301.01200/) (2022)
- [Multimodal SuperCon: Classifier for Drivers of Deforestation in Indonesia](https://mlsanity.com/paper/2207.14656/) (2022)
- [Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approach](https://mlsanity.com/paper/2207.13500/) (2022)
- [Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model](https://mlsanity.com/paper/2106.12226/) (2021)

## Abstract (arXiv)

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.