Sure based model selection for hyperspectral imaging

Abstract

Mean squared error (MSE) is commonly used for evaluating the performance of hyperspectral imaging (HSI) methods. MSE depends on the true (unknown) signal to be estimated and is therefore not computable for real data. Therefore, HSI methods are usually evaluated using simulated data. Stein’s unbiased risk estimator (SURE) is an unbiased estimator of the MSE that does not require knowledge of the true signal. The main aim of this paper is to promote the use of SURE for evaluating HSI models. To achieve that goal we compare three wavelet models, spectral, spatial and spectralspatial, for hyperspectral images. Hyperspectral images are modeled based on their sparse wavelet components. The penalized least squares with ‘1 penalty (to promote sparsity) is considered for sparse reconstruction. By comparing the SURE values for the three models, it is shown that the spatial model performs better than spectral model and spectralspatial model outperforms both spectral and spatial models.