Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years

2018/03/29 by John M. Wandeto, John Wandeto, Henry O. Nyongesa +5
Computer Science · Earth and Planetary Sciences · Engineering · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Remote Sensing and Land Use #Remote Sensing in Agriculture #Remote-Sensing Image Classification #cs.CV #cs.CY

paper · pdf · doi:10.48550/arxiv.1803.11125

arxiv created 2018/03/29 · openalex publication_date 2018/03/29 · arxiv updated 2018/03/30 · openalex created_date 2021/06/22 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• A Threshold Selection Method from Gray-Level Histograms
• On the detectability by novices, radiologists, and computer algorithms of smallest increases in local single dot size in random-dot images
• Using the quantization error from Self-Organized Map (SOM) output for detecting critical variability in large bodies of image time series in less than a minute

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