Jacob, A., Vicente-Guijalba, F., Lopez-Martinez, C., Lopez-Sanchez, J.M., Litzinger, M., Kristen, H., Mestre-Quereda, A., Ziolkowski, D., Lavalle, M., Notarnicola, C., Suresh, G., Antropov, O., Ge, S., Praks, J., Ban, Y., Pottier, E., Mallorqui, J., Duro, J. & Engdahl, M., “Sentinel-1 InSAR Coherence for LandCover Mapping: A comparison of multiple feature-based classifiers”, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 13, pp. 535-552, 2020.
This work investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyses the performance of this feature along with polarisation and intensity products according to different classification strategies and algorithms. Seven different classification work flows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain: interferometric coherence, backscattered intensities and polarisation. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximise diversity land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Doñana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%), obtained also promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherencebased results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinels-1A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1A/B stacks, i.e., 6-day sampling, are considered.