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Since the Solar Dynamics Observatory (SDO) began recording ~ 1 TB of data perday, there has been an increased need to automatically extract features andevents for further analysis. Here we compare the overall detection performance,correlations between extracted properties, and usability for feature trackingof four solar feature-detection algorithms: the Solar Monitor Active RegionTracker (SMART) detects active regions in line-of-sight magnetograms; theAutomated Solar Activity Prediction code (ASAP) detects sunspots and pores inwhite-light continuum images; the Sunspot Tracking And Recognition Algorithm(STARA) detects sunspots in white-light continuum images; the SpatialPossibilistic Clustering Algorithm (SPoCA) automatically segments solar EUVimages into active regions (AR), coronal holes (CH) and quiet Sun (QS). Onemonth of data from the SOHO/MDI and SOHO/EIT instruments during 12 May - 23June 2003 is analysed. The overall detection performance of each algorithm isbenchmarked against National Oceanic and Atmospheric Administration (NOAA) andSolar Influences Data Analysis Centre (SIDC) catalogues using various featureproperties such as total sunspot area, which shows good agreement, and thenumber of features detected, which shows poor agreement. Principal ComponentAnalysis indicates a clear distinction between photospheric properties, whichare highly correlated to the first component and account for 52.86% ofvariability in the data set, and coronal properties, which are moderatelycorrelated to both the first and second principal components. Finally, casestudies of NOAA 10377 and 10365 are conducted to determine algorithm stabilityfor tracking the evolution of individual features. We find that magnetic fluxand total sunspot area are the best indicators of active-region emergence.

A Multi-Wavelength Analysis of Active Regions and Sunspots by Comparison of Automated Detection Algorithms