Method for spectral data classification and detection in diverse lighting conditions

Abstract

The invention is a method of spectral data classification that uses the decoupling of target chromaticity and lighting or illumination chromaticity in spectral data and the sorting and selection of spectral bands by values of a merit function to obtain an optimized set of combinations of spectral bands for classification of the data. The decoupling is performed in 'delta-log' space. A rotation transform may be applied. For a broad range of parameters, correction of lighting chromaticity may be obtained by use of an equivalent 'Planck distribution' temperature. Merit function sorting and band combination selection is performed by multiple selection criteria. The method achieves reliable pixel classification and target detection in diverse lighting or illumination, especially in circumstances where lighting is non-uniform across a scene, such as with sunlight and shadows on a partly cloudy day or in “artificial” lighting. Applications are found in homeland security, defense, environmental protection, biomedical diagnostics, industrial process and product monitoring, and other remote or standoff sensing by spectral characteristics.