期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2022
卷号：V-1-2022
页码：49-56
DOI：10.5194/isprs-annals-V-1-2022-49-2022
语种：English
出版社：Copernicus Publications
摘要：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.
关键词：Multi-frequency PolInSAR; F-SAR; Land Cover Classification; Supervised Dimension Reduction; UMAP
