Employing Spatial Metrics in Urban Land-use/Land-cover Mapping
We examine the potential of supplementing per-pixel classifiers with the Getis index (Gi) in comparison to the Geary’s C on a subset of Ikonos imagery for urban land-use and landcover classification. The test is pertinent considering that the Gi is generally considered more
capable of identifying clusters of points with similar attributes. We quantify the impact of varying distance thresholds on the classification product and demonstrate how well the Gi identified cold and hot spots in comparison to Geary’s C. The exercise also provides a rule of thumb
for effectively measuring spatial association in connection to adjacency. We are able to support existing literature that measuring local variability improves classification over spectral information alone. The results, however, neither confirm nor deny the challenge on whether measuring cold
and hot spots rather than just spatial association improves classification accuracy.
Document Type: Research Article
Publication date: 01 December 2007
- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies. - Editorial Board
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