Summary of In the Search For Optimal Multi-view Learning Models For Crop Classification with Global Remote Sensing Data, by Francisco Mena et al.
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data
by Francisco Mena, Diego Arenas, Andreas Dengel
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates Multi-View Learning (MVL) in crop classification, which faces challenges when dealing with multiple inputs. The authors propose a novel approach that simultaneously selects the fusion strategy and encoder architecture to classify cropland and crops at global and local scales. They evaluate five fusion strategies and five temporal encoders on the CropHarvest dataset, which provides various input data types. Results show that no single configuration is sufficient for all scenarios, emphasizing the need for a tailored combination of encoder and fusion strategy. The authors suggest a methodological framework for MVL-based crop classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how to accurately classify different types of crops using information from multiple sources. Currently, it’s difficult to analyze cropland because it grows in different ways depending on the location. Researchers have tried using deep learning models, but they struggle when dealing with lots of data. The authors propose a new approach that combines two things: how the data is combined (fusion strategy) and what kind of computer model is used (encoder architecture). They test this approach on a dataset called CropHarvest, which has information from many different sources. The results show that no single combination works best for all types of crops, so they suggest finding the right combination for each specific type. |
Keywords
* Artificial intelligence * Classification * Deep learning * Encoder
