Sparse Unmixing-Based Content Retrieval of Hyperspectral Images on Graphics Processing Units
Content-based image retrieval (CBIR) systems have gained significant importance in the remotely sensed hyperspectral imaging community due to the increasing availability of hyperspectral data collected from different instruments. Spectral unmixing has been a popular technique for not only interpreting hyperspectral images but also retrieving them precisely from databases based on information content. This is due to the fact that the information provided by unmixing (i.e., the spectrally pure components of the scene or endmembers, and their corresponding abundance fractions) provides a very intuitive way to describe the content of the scene in both the spectral and the spatial sense. In this letter, we present a new computationally efficient CBIR system for hyperspectral data (available online: http://hypercomp. es/repositorySparse) which uses sparse unmixing concepts to retrieve hyperspectral scenes, based on their content, from large repositories. The search is guided by a spectral library, which is used as a guide to retrieve the data in a robust and efficient way. Given the large size of libraries and the sparsity of the unmixing solutions, the incorporation of sparse unmixing to the CBIR engine brings significant advantages. To optimize its performance in computational terms, the system has been implemented in parallel by taking advantage of the computational power of commodity graphics processing units. The proposed system is validated using a collection of synthetic and real hyperspectral images, exhibiting state-of-the-art performance.