Study Cases on Fast Compression Distance Based Data Visualization

In this paper, we develop a visualization tool to enhance the understanding of up to big data sets. Compared to classic data models which rely on the computing of the features (color, texture, etc.), this tool is fully feature free, as it processes directly on the data file. The Fast Compression Distance (FCD) and t-distributed Stochastic Neighbor Embedding (t-SNE) have been applied to visualize a large TerraSAR-X dataset which are annotated with up to three layers of hierarchical semantic labels, and a Sentinel-1 dataset with 10 annotated classes, in VV and VH polarization modes. We analyze the visualization results in manifold space, and try to understand and interpret them with the available semantic labels. The visualization interpretation is based on a vega-style interactive tool, which allows user zoom in, zoom out for processing large amount of data points.