Abstract
Abstract. This 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.