Wavelet transform for dimensionality reduction in hyperspectral linear unmixing

In Li et al. (2001), the authors investigated how dimensionality reduction using wavelet-based feature extraction can improve the classification of materials from hyperspectral reflectance. In this paper, a similar approach is suggested for the hyperspectral linear unmixing problem. The paper shows, both experimentally and theoretically, that the abundance estimation using the least squares estimation can be improved through appropriate feature extraction. The discrete wavelet transform is suggested for the feature extraction, and a wavelet-based unmixing system is designed and implemented. Two metrics, the root-mean-square error and the confidence of abundance estimation, are proposed to quantitatively evaluate the unmixing system performance.