سال انتشار: ۱۳۹۱

محل انتشار: بیستمین کنفرانس مهندسی برق ایران

تعداد صفحات: ۵

نویسنده(ها):

Habib Alizadeh – Faculty of Electrical and Computer Engineering, Tarbiat Modares University
Hassan Ghassemian –

چکیده:

One of problems that have been not considered in unmixing process of hyperspectral is the correlation between bands. This correlation makes difficult the unmixing of spectralsignatures of different materials. Furthermore, the large number of spectral bands extends the execution time of the unmixing process. In this paper, a new approach for the unmixing of hyperspectral data using the semi-Nonnegative Matrix Factor (semi-NMF) and Principal Component Analysis (PCA) isproposed that solves the problem of correlation between bands and decrease execution time of algorithm. The proposedapproach uses from PCA of data in the unmixing process instead of original data. Using this linear transformation, the images are mapped to the uncorrelated space. Uncorrelated images make more efficient the unmixing process. In order to overcome the problem of non-uniqueness solution that is caused by the nonconvex cost function, the smoothness and sparseness constraints are introduced to the semi-NMF. In addition to its high accuracy, the proposed method increases the speed of the unmixing process. The experimental results show excellence of the proposed approach in comparison of other methods