Abstract:

In hyperspectral images, pixels are found as a mixture of the spectral signatures of several materials, especially when there is an insufficient spatial resolution. In recent years, spectral libraries have provided spectral information of hundreds of materials that allow the development of techniques to solve the problem of hyperspectral unmixing in a semi-supervised fashion. These methods which are also known as sparse regression techniques assume that mixed pixels are a sparse linear combination of spectral signatures of materials in already available spectral libraries. In this paper, the spectral mixing problem has been solved via sparse separation methods. The United States Geological Survey (USGS) spectral library is used to generate simulated hyperspectral data. A comparative analysis is performed to determine which material signatures in the library are mixed in the pixels by using the convex-relaxation-based sparse regression methods. Root Mean Square Error (RMSE), Signal to Reconstruction Error (SRE) and processing time of the algorithms are used as comparing criterions. Moreover, Hinton diagrams are used to visualize which material signatures are found in the library and the proportions of these found material signatures.

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