Feature-Driven Multilayer Visualization for Remotely Sensed Hyperspectral Imagery

Displaying the abundant information contained in a remotely sensed hyperspectral image is a challenging problem. Currently, no approach can satisfactorily render the desired information at arbitrary levels of detail. In this paper, we present a feature-driven multilayer visualization technique that automatically chooses data visualization techniques based on the spatial distribution and importance of the endmembers. It can simultaneously visualize the overall material distribution, subpixel level details, and target pixels and materials. By incorporating interactive tools, different levels of detail can be presented per users´ request. This scheme employs five layers from the bottom to the top: the background layer, data-driven spot layer, pie-chart layer, oriented sliver layer, and anomaly layer. The background layer provides the basic tone of the display; the data-driven spot layer manifests the overall material distribution in an image scene; the pie-chart layer presents the precise abundances of endmember materials in each pixel; the oriented sliver layer emphasizes the distribution of important anomalous materials; and the anomaly layer highlights anomaly pixels (i.e., potential targets). Displays of the airborne AVIRIS data and spaceborne Hyperion data demonstrate that the proposed multilayer visualization scheme can efficiently display more information globally and locally.