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Data fusion is the process of integration of multiple data and knowledge representing the same real-world object into a consistent, accurate, and useful representation. Data fusion processes are often categorized as low, intermediate or high, depending on the processing stage at which fusion takes place. Low level data fusion combines several sources of raw data to produce new raw data. The expectation is that fused data is more informative and synthetic than the original inputs. For example, sensor fusion is also known as (multi-sensor) data fusion and is a subset of information fusion. ==Geospatial applications== In the geospatial (GIS) domain, data fusion is often synonymous with data integration. In these applications, there is often a need to combine diverse data sets into a unified (fused) data set which includes all of the data points and time steps from the input data sets. The fused data set is different from a simple combined superset in that the points in the fused data set contain attributes and metadata which might not have been included for these points in the original data set. A simplified example of this process is shown below where data set "α" is fused with data set β to form the fused data set δ. Data points in set "α" have spatial coordinates X and Y and attributes A1 and A2. Data points in set β have spatial coordinates X and Y and attributes B1 and B2. The fused data set contains all points and attributes Input Data Set α Input Data Set β Fused Data Set δ In this simple case all attributes are uniform across the entire analysis domain, so attributes may be simply assigned. In more realistic applications, attributes are rarely uniform and some type of interpolation is usually required to properly assign attributes to the data points in the fused set. In a much more complicated application, marine animal researchers use data fusion to combine animal tracking data with bathymetric, meteorological, sea surface temperature (SST) and animal habitat data to examine and understand habitat utilization and animal behavior in reaction to external forces such as weather or water temperature. Each of these data sets exhibit a different spatial grid and sampling rate so a simple combination would likely create erroneous assumptions and taint the results of the analysis. But through the use of data fusion, all data and attributes are brought together into a single view in which a more complete picture of the environment is created. This enables scientists to identify key locations and times and form new insights into the interactions between the environment and animal behaviors. In the figure at right, rock lobsters are studied off the coast of Tasmania. Dr. Hugh Pederson of the University of Tasmania used data fusion software to fuse southern rock lobster tracking data (color-coded for in yellow and black for day and night, respectively) with bathymetry and habitat data to create a unique 4D picture of rock lobster behavior. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「data fusion」の詳細全文を読む スポンサード リンク
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