What is Data Fusion?
Data Fusion is the process of integrating data from multiple sources to create a more comprehensive and accurate representation of the information. Data fusion can involve combining data from different sensors, databases, or data streams to obtain a more complete view of a particular subject or phenomenon. Data fusion techniques can be applied to various fields, such as remote sensing, robotics, medical imaging, and sensor networks. The main goal of data fusion is to improve the quality and reliability of the information derived from the data, which can lead to better decision-making and more accurate predictions.
Data Fusion Techniques
There are several techniques for data fusion, including:
- Feature-level fusion: Combining features extracted from different data sources to create a new, higher-dimensional feature representation.
- Decision-level fusion: Aggregating the outputs or decisions from multiple models or classifiers to arrive at a final decision.
- Model-level fusion: Combining multiple models or algorithms to create a more robust and accurate prediction model.
- Data-level fusion: Integrating raw data from multiple sources before processing and analysis.
Resources for Learning More about Data Fusion
To learn more about data fusion and its applications, you can explore the following resources:
