Combining synthetic aperture radar and multispectral images for land cover classification: a case study of Beijing, China

Abstract

We propose exploratory research combining synthetic aperture radar (SAR) data, represented by Sentinel-1A, and multispectral data, represented by Landsat-8 operational land imager (OLI), to demonstrate the applicability and effectiveness of land cover classification based on a Beijing case study. The proposed method consists of two phases. In the fusion phase, we select three methods to evaluate the performance of integrated Sentinel-1A and Landsat-8 OLI images. In the classification phase, we choose four common methods to examine the classifying capability hidden within the fused images. Experimental results indicate that the Gram-Schmidt spectral sharpening is superior in terms of maintaining the geometric structure, spectral texture, and spatial information, demonstrating a better fusion effect than other methods. The support vector machine classification exhibits the best performance of the fused images, with an overall classification accuracy of 94.01% and a kappa coefficient of 0.91. The fused images provide better classification potential as they benefit from having more spatial information and spectral information distribution, and the mean value of overall classification accuracy and the kappa coefficient are on average 5.61% and 0.08 higher, respectively, than the original Landsat-8. Finally, we conclude that the integrated use of SAR and multispectral images significantly improves classification accuracies, thus making it effective for land cover information extraction.