Geometric- and Optimization-Based Registration Methods for Long-Wave Infrared Hyperspectral Images

Registration of long-wave infrared (LWIR) hyperspectral images with their thermal and emissivity components has until now received comparatively less attention with respect to the visible near and short wave infrared hyperspectral images. In this paper, the registration of LWIR hyperspectral images is investigated to enhance applications of LWIR images such as change detection, temperature and emissivity separation, and target detection. The proposed approach first searches for the best features of hyperspectral image pixels for extraction and matching in the LWIR range and then performs a global registration over two-dimensional maps of three-dimensional hyperspectral cubes. The performances of temperature and emissivity features in the thermal domain along with the average energy and principal components of spectral radiance are investigated. The global registration performed over whole 2D maps is further improved by blockwise local refinements. Among the two proposed approaches, the geometric refinement seeks the best keypoint combination in the neighborhood of each block to estimate the transformation for that block. The alternative optimization-based refinement iteratively finds the best transformation by maximizing the similarity of the reference and transformed blocks. The possible blocking artifacts due to blockwise mapping are finally eliminated by pixelwise refinement. The experiments are evaluated with respect to the (i) utilized similarity metrics in the LWIR range between transformed and reference blocks, (ii) proposed geometric- and optimization-based methods, and (iii) image pairs captured on the same and different days. The better performance of the proposed approach compared to manual, GPU-IMU-based, and state-of-the-art image registration methods is verified.