Object Detection by Spectropolarimeteric Imagery Fusion

In the past few years, imaging spectroscopy has been widely used. However, it only acquires intensity information in a narrow electromagnetic band, ignoring the polarimetric information of the electromagnetic wave and resulting in inaccurate object detection. According to electromagnetic theory, the reflected spectral signature depends on the elemental composition of objects residing within the scene, and the radiation's polarization characteristic is sensitive to surface features, such as relative smoothness and conductance. Independently, spectral and polarimetric features give incomplete representations of an object of interest. These representations are complementary, and it is expected that the combination of complementary information will reduce false alarms, improve confidence in target identification, and improve the quality of the scene description. Imaging spectropolarimetric technology as a new sensing method can acquire polarimetric information at narrow electromagnetic bands, but there are a few results showing how to combine these complementary features to detect objects in clutter. In this paper, a spectropolarimetric projection scheme is proposed to divide the spectropolarimetric data set into two parts: 1) a polarimetric spectrum data set and 2) a polarimetric data cube. Then, the polarimetric spectrum anomaly feature extraction method is used to deal with the polarimetric spectrum data set, and the adaptive polarimetric information fusion method is proposed to extract the feature from the polarimetric data cube. Finally, these features are combined by the Choquet integral to achieve better detection performance. The algorithm is applied to one complex scene, and detailed detection performance is evaluated.