Improvement in Satellite Image-Based Land Cover Classification with Landscape Metrics

Abstract

The use of an object-based image analysis (OBIA) method has recently become quite common for classifying high-resolution remote-sensed images. However, despite OBIA’s segmentation being equally useful for analysing medium-resolution images, it is not used for them as often. This study aims to analyse the effect of landscape metrics that have not yet been used in image classification to provide additional information for land cover mapping to improve the thematic accuracy of satellite image-based land cover mapping. To this end, multispectral satellite images taken by Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) during three different seasons in 2017 were analysed. The images were segmented, and based on these segments, four patch-level landscape metrics (mean patch size, total edge, mean shape index and fractal dimension) were calculated. A random forest classifier was applied for classification, and the Coordination of Information on the Environment Land Cover (CLC) 2018 database was used as reference data. According to the results, landscape metrics both with and without segmentation can significantly improve the overall accuracy of the classification over classification based on spectral values. The highest overall accuracy was achieved using all data (i.e., spectral values, segmentation, and metrics).