Noise Reduction on Hyperspectral Imagery Using Spectral Unmixing and Class-Labels

Noise reduction on hyperspectral imagery is a critical step for the success of other applications that use this type of data. In this paper, we propose a novel approach to reduce the noise on hyperspectral data that might occur due to various factors. Since the proposed method exploits class-labels of data, it can be categorized as a semi-supervised method. First, our approach computes the mean spectral signatures of data using their spatial coherence and class-labels, then robust pure material signatures are estimated with different spectral unmixing methods. Later, these signatures are analyzed for the noise reduction. Tests are conducted on Indian Pines dataset under different noise characteristics. The experimental results show that our proposed method improves PSNR scores compared to baseline methods that use either spectral unmixing or class-labels. Furthermore, noticeable improvements on computation complexity are observed.