Weighting Function Alternatives for a Subpixel Allocation Model
This study investigates the “pixel-swapping” optimization algorithm proposed by Atkinson for predicting subpixel land-cover distribution. Two limitations of this method are assessed: the arbitrary spatial range value and the arbitrary exponential model for characterizing
spatial autocorrelation. Various alternative weighting functions are evaluated. For this assessment, two different simulation models are employed to develop spatially autocorrelated binary class raster maps. These rasters are then resampled to generate sets of representative medium-resolution
class maps. Prior to conducting the subpixel allocation, the relationship between cell resolution and spatial autocorrelation, as measured by Moran’s I, is evaluated. It is discovered that the form of this relationship depends upon the simulation model. For all tested weighting
functions (Nearest Neighbor, Gaussian, Exponential, and IDW), the pixel swapping method increased classification accuracy compared with the initial random allocation of subpixels. Nearest Neighbor allocation performs as well as the more complex models of spatial structure.
Document Type: Research Article
Publication date: 01 November 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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