Evaluating Uniform Manifold Approximation and Projection for Dimension Reduction and Visualization of Polinsar Features

Abstract

Abstract. In this paper, the nonlinear dimension reduction algorithm Uniform Manifold Approximation and Projection (UMAP) is investigated to visualize information contained in high dimensional feature representations of Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data. Based on polarimetric parameters, target decomposition methods and interferometric coherences a wide range of features is extracted that spans the high dimensional feature space. UMAP is applied to determine a representation of the data in 2D and 3D euclidean space, preserving local and global structures of the data and still suited for classification. The performance of UMAP in terms of generating expressive visualizations is evaluated on PolInSAR data acquired by the F-SAR sensor and compared to that of Principal Component Analysis (PCA), Laplacian Eigenmaps (LE) and t-distributed Stochastic Neighbor embedding (t-SNE). For this purpose, a visual analysis of 2D embeddings is performed. In addition, a quantitative analysis is provided for evaluating the preservation of information in low dimensional representations with respect to separability of different land cover classes. The results show that UMAP exceeds the capability of PCA and LE in these regards and is competitive with t-SNE.

Topological Characteristics of the Pore Network in the Tight Sandstone Using Persistent Homology

“…The uniform manifold approximation and projection (UMAP) algorithm has proven to be effective in processing data sets that contain noise and outliers while preserving important structural features. By utilizing UMAP for dimensionality reduction, the visualization and analysis of the topological structure in point cloud data can be improved and topological differences across different data sets can be compared …”

“…By utilizing UMAP for dimensionality reduction, the visualization and analysis of the topological structure in point cloud data can be improved and topological differences across different data sets can be compared. 25 …”

“…By utilizing UMAP for dimensionality reduction, the visualization and analysis of the topological structure in point cloud data can be improved and topological differences across different data sets can be compared. 25 To construct the initial high-dimensional graph, UMAP introduces the concept of a "fuzzy simple complex″. This concept represents a weighted graph, in which the edge weights indicate the likelihood of connecting two points.…”

Multi-Frequency Polinsar Data Are Advantageous for Land Cover Classification – A Visual and Quantitative Analysis

“…In contrast to existing approaches, the method applied in this paper employs Uniform Manifold Approximation and Projection (UMAP) for dimension reduction. This algorithm is chosen, since, according to our analysis presented in (Schmitz et al, 2021), it is well suited for finding a 3-dimensional representation of PolInSAR data that preserves class separability.…”