Retrieval of multi- and hyperspectral images using an interactive relevance feedback form of content-based image retrieval

This paper demonstrates the capability of a set of image search algorithms and display tools to search large databases for multi- and hyperspectral image cubes most closely matching a particular query cube. An interactive search and analysis tool is presented and tested based on a relevance feedback approach that uses the human-in-the-loop to enhance a content-based image retrieval process to rapidly find the desired set of image cubes.

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