Figure 7 - available via license: Creative Commons Attribution 4.0 International
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An example of how the Gaussian blur filter is able to remove isolated pixel detections, which we assume are false positives, leaving the spatially continuous detection clusters. Yellow points are pixels that our algorithm has highlighted as having a change from steady-state (offset or gradient) at this time (28 July 2018). Purple colours are either NaN pixels or pixels with no changes in their displacements. a) Shows the raw detections using our algorithm, and b) the results after spatial filtering. c) are the spatially isolated pixels that were removed by the filter. Axis represent the rows and columns of the pixels
Source publication
Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. Thanks to regular acquis...
Context in source publication
Context 1
... remove isolated pixel detections, which we assume are false positives, by filtering the detections for each time stamp using a Gaussian blur in a kernel window of approximately 200 m. On average, spatial filtering resulted in a 26% reduction in the number of change detections across the entire stack (Figure 7). ...
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Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. Thanks to regular acquis...
