Hyperspectral unmixing using statistics of Q function

Proposed technique of hyperspectral unmixing is apparent to implement and compute the results in a very fast and efficient manner. To reducing the computational complexity and to estimation of hyperspectral data we adopted a statistical method of median absolute deviation about median. Number of end-members is enumerating by self iterative subspace projection method which depends on Pearson correlation. The mixing matrix is inferred by using Q function projections. A set of tests with real hyperspectral data evaluates the performance and illustrates the effectiveness of the proposed method. For the evaluation of proposed method, the results are compared with the results of vertex component analysis. The experimental results show the effectiveness of proposed method on hyperspectral unmixing. targets Alunite, Buddingtonite, Calcite, Kaolinite, and Muscovite are detected well and have high spectral similarities. Hyperspectral remote sensing is used in a large array of real life applications e.g. Surveillance, Mineralogy, Physics, and Agriculture. The complete work is prepared by using MATLAB.