Adaptive spatial reclassification kernels for urban mapping from remotely sensed data: the A-SPARK approach

摘要

The existing spatial reclassification kernel (SPARK) approach provides a simple and practical procedure for discrimination of complex land use classes from the primary land cover components. Previous works have shown that the spatial information extracted from a single kernel size often does not lead to a satisfactory result. Due to the complexity and diversity of most objects of interest, this limitation is more significant in urban dominated landscapes. To overcome this limitation, an adaptive approach for implementation of SPARK based on the automatic evaluation and selection of kernels has been developed in this research. Efficiency of the proposed approach for discrimination of spectrally confused and complex classes such as high-density and low-density residential areas has been evaluated by using SPOT data acquired from part of Tehran's metropolitan areas, Iran. Results of the practical examination have shown that considerable improvements in the classification accuracy of different classes such as high-density residential, low-density residential, industrial, orchards and bare lands can be achieved. The overall accuracy of classification has increased from 82.39% to 92% in the best fixed kernel size of 9 × 9; this is an indicator of the more effective information use in the proposed approach.