Multimodal detection of man-made objects in simulated aerial images

This paper presents an approach to multi-modal detection of man-made objects from aerial imagery. Detections are made in polarization imagery, hyperspectral imagery, and LIDAR point clouds then fused into a single confidence map. The detections are based on reflective, spectral, and geometric features of man-made objects in airborne images. The polarization imagery detector uses the Stokes parameters and the degree of linear polarization to find highly polarizing objects. The hyperspectral detector matches scene spectra to a library of man-made materials using a combination of the spectral gradient angle and the generalized likelihood ratio test. The LIDAR detector clusters 3D points into objects using principle component analysis and prunes the detections by size and shape. Once the three channels are mapped into detection images, the information can be fused without some of the problems of multi-modal fusion, such as edge reversal. The imagery used in this system was simulated with a first-principles ray tracing image generator known as DIRSIG.