Data fusion refers to the process of integrating multiple sources of data or information from different sensors, modalities, or platforms to provide a more complete and accurate representation of a phenomenon or situation.
It involves combining data with varying characteristics, such as spatial resolution, temporal frequency, and thematic content, to derive new insights or improve understanding of a complex system or event.
Data Fusion works by collecting data from disparate sources and integrating it using algorithms and techniques that explain the strengths and weaknesses of each data set. The goal is to produce merged data that is more informative, reliable and actionable than individual datasets alone.
Techniques used in data fusion include statistical methods, machine learning algorithms, sensor calibration, and spatial-temporal alignment to reconcile differences in data characteristics and achieve a consistent representation.
The steps in data fusion typically include preprocessing, where raw data is cleaned, calibrated and standardized; Extraction of features, if any relevant information, extracted from each dataset; Registration and alignment, where datasets are synchronized spatially and temporally; Fusion algorithm application, where built-in algorithms combine datasets; and finally, results interpretation and validation, where the merged data is analyzed and validated for accuracy and consistency.
Dataset fusion refers to the specific process of integrating multiple data sets that come from different sources or modalities into a unified data set.
This integration may involve datasets acquired from different sensors, platforms or time periods, aiming to create a more comprehensive dataset with improved coverage, resolution or information content. Dataset fusion is essential in fields such as remote sensing, environmental monitoring and intelligence analysis, where combining data from various sources improves the accuracy and reliability of analysis processes and decision-making