Date of Original Version
Natural Resources Science
This paper introduces a Subpixel Proportional Land cover Information Transformation (SPLIT) model to extract proportions of impervious surfaces in urban and suburban areas. High spatial resolution airborne Digital Multispectral Videography (DMSV) data provided subpixel information for Landsat TM data. The SPLIT model employed a Modularized Artificial Neural Network (MANN) to integrate multi-sensor remote sensing data and to extract proportions of impervious surfaces and other types of land cover within TM pixels. Through a control unit, the MANN was able to decompose a complex task into multiple subtasks by using a group of sub-networks. The SPLIT model identified spectral relations between TM pixel values and the corresponding DMSV subpixel patterns. The established relationship allows extrapolation of the SPLIT model to the areas beyond DMSV data coverage. We applied five intervals, i.e., <20 percent, 21 to 40 percent, 41 to 60 percent, 61 to 80 percent, and >81 percent, to map the subpixel proportions of land cover types. We extrapolated the SPLIT model from training sites that have both TM and DMSV coverage into the entire DuPage County with TM data as the input. The extrapolation received 82.9 percent overall accuracy for the extracted proportions of urban impervious surface.
Publication Title, e.g., Journal
Photogrammetric Engineering & Remote Sensing
Wang, Y. & Zhang, X. (2004). A SPLIT Model for Extraction of Subpixel Impervious Surface Information. Photogrammetric Engineering & Remote Sensing, 7, 821-828. https://doi.org/10.14358/PERS.70.7.821
Available at: https://doi.org/10.14358/PERS.70.7.821
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