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Analysis of Spatial-Spectral Relationships in Real Hyperspectral Imaging to use for Hyperspectral Unmixing.
This research proposes a methodology to perform unsupervised unmixing establishing how the spatial information help to capture the relationship between the grade of uniformity of the clusters, and the convex regions in the image data set. The effect of splitting the image helps us to obtain homogeneous regions. To achieve the localization of the endmember, principal component analysis is used, and the first three of them containing about 96% of the total information of hyperspectral image and then they are plotted for visualization their behavior.
This analysis help us to understand the relation between the spatial domain information and data cloud structure. We saw experimentally that by partitioning the image in homogeneous regions we can decompose the data cloud in piece wise convex regions. We can then apply linear unmixing to these regions and easily extract endmembers for different homogeneous tiles in the image and shows how to perform hyperspectral unmixing using local information and merge them at a global level to develop an accurate description of the scene under study.
Author(s):
Miguel A. Goenaga-Jimenez
Electrical and Computer Engineering
Universidad del Turabo
Puerto Rico
Eduardo Castillo-Charris
Mechanical Engineering
Universidad del Turabo
Puerto Rico