Abstract
Abstract. The aim of this research is to classify urban land cover types using an advanced classification method. As the input bands to the classification, the features derived from Landsat 8 and Sentinel 1A SAR data sets are used. To extract the reliable urban land cover information from the optical and SAR features, a rule-based classification algorithm that uses spatial thresholds defined from the contextual knowledge is constructed. The result of the constructed method is compared with the results of a standard classification technique and it indicates a higher accuracy. Overall, the study demonstrates that the multisource data sets can considerably improve the classification of urban land cover types and the rule-based method is a powerful tool to produce a reliable land cover map.
Highlights
Remote sensing (RS) has provided an important source of information for urban land use and land cover classification, since the appearance of the first digital data sets (Stavrakoudis et al, 2011, Amarsaikhan et al, 2018)
The studies have shown that SAR images may be the excellent basis for classifying, monitoring and analyzing urban conglomerations and their development over time especially, a large area mapping is under consideration (Dell’Acqua, 2009, Taubenböck et al, 2012, Amarsaikhan et al, 2018)
It is clear that a combined use of the optical and SAR images will have a number of advantages because a specific urban feature, which is not seen on the passive sensor image may be observable on the microwave image and vice versa because of the complementary information provided by the two sources (Amarsaikhan et al, 2012, Amarsaikhan, Ganchuluun, 2015)
Summary
Remote sensing (RS) has provided an important source of information for urban land use and land cover classification, since the appearance of the first digital data sets (Stavrakoudis et al, 2011, Amarsaikhan et al, 2018). In urban area mapping, for differentiation of the spectrally similar or mixed classes, reliable features derived from multiple sources and an efficient classification technique should be used (Amarsaikhan et al, 2012). The aim of this study is to classify the features derived from optical and SAR data sets and produce a reliable urban land cover map using a rule-based classification method
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