Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
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Independent Component Analysis (ICA) is a blind source separation method which is exploited for various applications in signal processing. In hyperspectral imagery, ICA is commonly employed for detection and segmentation purposes. But it is often thought to be unable to quantify abundances. In this paper, we propose an ICA-based method to estimate the anomaly abundances from the independent components. The first experiments on synthetic and real world hyperspectral images are very promising referring to the estimation accuracy and robustness.