Hyperspectral Image Visualization Using Double And Multiple Layers

This dissertation develops new approaches for hyperspectral image visualization. Double and multiple layers are proposed to effectively convey the abundant information contained in the original high-dimensional data for practical decision-making support. The contributions of this dissertation are as follows. 1.Development of new visualization algorithms for hyperspectral imagery. Double-layer technique can display mixed pixel composition and global material distribution simultaneously. The pie-chart layer, taking advantage of the properties of non-negativity and sum-to-one abundances from linear mixture analysis of hyperspectral pixels, can be fully integrated with the background layer. Such a synergy enhances the presentation at both macro and micro scales. 2.Design of an effective visual exploration tool. The developed visualization techniques are implemented in a visualization system, which can automatically preprocess and visualize hyperspectral imagery. The interactive tool with a userriendly interface will enable viewers to display an image with any desired level of details. 3.Design of effective user studies to validate and improve visualization methods. The double-layer technique is evaluated by well designed user studies. The traditional approaches, including gray-scale side-by-side classification maps, color hard classification maps, and color soft classification maps, are compared with the proposed double-layer technique. The results of the user studies indicate that the double-layer algorithm provides the best performance in displaying mixed pixel composition in several aspects and that it has the competitive capability of displaying the global material distribution. Based on these results, a multi-layer algorithm is proposed to improve global information display