A robust spectral target recognition method for hyperspectral data based on combined spectral signatures

Abstract

Achieving high target recognition accuracy is a pursuing and challenging issue for hyperspectral data analysis. The complicated imaging environment and noise interference lead to heterogeneous spectra within the homogeneous object, which makes the current spectral target recognition methods be lack of robustness. In this paper, a robust spectral target recognition method is proposed based on the combined spectral signatures, in which two key techniques are concerned. One is support vector data description (SVDD), which can tolerate the spectral variations of different pixels in the same object. Another is an effective spectral signature combined of spectral reflectance and spectral derivative, which can be robust to data characteristics with different spectral-amplitude variation. The proposed method outperforms the classical methods with only spectral reflective information in term of target recognition accuracy and robustness.