Inferring urban land use using the optimised spatial reclassification kernel

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 found in the neighbourhood of a pixel to be classified, resulting in the Optimised SPARK (OSPARK) algorithm. The performance of SPARK and OSPARK for land-use classification has been evaluated for the Dublin urban area (Ireland), using a Landsat TM image. The MOLAND land-use map of 1990 was used as a reference. Results show that the use of optimal kernel sizes instead of fixed kernel sizes reduces the omission and commission errors for most land-use classes.

► 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.