Inferring urban land use using the optimised spatial reclassification kernel

Publication year: 2011
Source: Environmental Modelling & Software, In Press, Corrected Proof, Available online 14 June 2011

Johannes, van der Kwast , Tim, Van de Voorde , Frank, Canters , Inge, Uljee , Stijn, Van Looy , …

In the 1990s, promising results in land-use classification were obtained by kernel-based contextual classification algorithms. Soon, however, it was recognised that kernel-based reclassifiers have important shortcomings and research instead focused on object-based image analysis. This study proposes a solution to two of the most important drawbacks of kernel-based reclassifiers: (1) the use of kernels tends to smooth boundaries between discrete land-use/land-cover parcels, and (2) it is difficult to determine a priori the optimum kernel size of the classifier. The Spatial Reclassification Kernel (SPARK) algorithm has been adapted in order to automatically optimise the kernel size depending on the spatial variation…

 Highlights: ► Major shortcomings of kernel-based reclassification algorithms were solved. ► An algorithm that optimises the kernel size for each pixel has been developed. ► Using optimal instead of fixed kernel sizes improves the reclassification results.