CC BYSchmitz, SylviaSylviaSchmitzHammer, HorstHorstHammerThiele, AntjeAntjeThiele2022-08-032022-08-032022https://publica.fraunhofer.de/handle/publica/41929810.5194/isprs-annals-v-1-2022-49-2022This paper investigates the enhanced potential of using multi-frequency PolInSAR data for land cover classification. In order to enable a descriptive analysis that goes beyond the mere comparison of classification accuracies, a two-step classification process is applied. First, polarimetric and interferometric features are extracted and projected into a 3-dimensional feature space by using the supervised dimension reduction algorithm Uniform Manifold Approximation and Projection (UMAP). Subsequently, based on the expressive 3-dimensional representation a simple yet sufficient k-nearest neighbors (KNN) classifier is applied to assign a land cover class to each pixel. In this way, besides the simplified classification, the visualization of the underlying data structure is possible and contributes to a better explanation and analysis of classification results. The data analyzed in this way are airborne L- and S-band PolInSAR data acquired by the F-SAR system. The visual analysis of reduced feature spaces as well as the quantitative analysis of classification results reveal the benefits of combining both frequencies with regard to class separability.enMulti-frequency PolInSARF-SARLand Cover ClassificationSupervised Dimension ReductionUMAPconference paper