Fuse spectropolarimetric imagery by D-S reasoning

The detection of low signature objects in cluttered backgrounds is a crucial problem in remote sensing. In the past few years, imaging spectral and polarimetric sensors have been evaluated for this application. As the reflection or emission spectral signatures depend on the elemental composition of objects residing within the scene. The polarization state of radiation is sensitive to surface features such as relative smoothness or roughness. But each character (spectral, polarimetric or spatial character) giving an incomplete representation of an object of interest, it expected that the combination of complementary and redundant characters would be contributed to reduce the false alarm rate, improve the confidence in the target identification and the quality of the scene description as a whole. Imaging spectropolarimetry provides effective mean to acquire spatial, spectral and polarimetric information of scene. This paper presents a study of spectropolarimetric image data set recorded from imaging spectropolarimeter located on top of building. The low probability detection algorithm was separately applied to polarimetric data sets of each band (Stokes images, degree of polarization image and angle of polarization image) to obtain a series of two dimensional map of objects and false detection. As there are some conflictions among these maps, D-S reasoning is used to combine these maps to improve the detection rate and low false rate. Through experiment and simulation, we conclude that this fusion algorithm can be well applied to enhance the detection performance.