Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years
This incremental method provides a fast and reliable approach for detecting critical changes in time-series satellite images, which could benefit citizens, historians, or policymakers interested in environmental or urban landscape monitoring.
The authors tackled the problem of detecting structural changes in satellite images over time by applying Self Organized Maps (SOM) and quantization error (QE) analysis to Las Vegas and Lake Mead from 1984 to 2008, showing that QE can reliably measure variability and detect changes in magnitude and direction with statistical trend analysis.
Time-series of satellite images may reveal important data about changes in environmental conditions and natural or urban landscape structures that are of potential interest to citizens, historians, or policymakers. We applied a fast method of image analysis using Self Organized Maps (SOM) and, more specifically, the quantization error (QE), for the visualization of critical changes in satellite images of Las Vegas, generated across the years 1984-2008, a period of major restructuration of the urban landscape. As shown in our previous work, the QE from the SOM output is a reliable measure of variability in local image contents. In the present work, we use statistical trend analysis to show how the QE from SOM run on specific geographic regions of interest extracted from satellite images can be exploited to detect both the magnitude and the direction of structural change across time at a glance. Significantly correlated demographic data for the same reference time period are highlighted. The approach is fast and reliable, and can be implemented for the rapid detection of potentially critical changes in time series of large bodies of image data.