An Improved Registration Method for UAV-Based Linear Variable Filter Hyperspectral Data - Xiao Wang; Chunyao Yu; Xiaohong Zhang; Xue Liu; Yinxing Zhang; Junyong Fang; Qing Xiao (Remote Sensing)
Linear Variable Filter (LVF) hyperspectral cameras possess the advantages of high spectral resolution, compact size, and light weight, making them highly suitable for unmanned aerial vehicle (UAV) platforms. However, challenges arise in data registration due to the imaging characteristics of LVF data and the instability of UAV platforms. These challenges stem from the diversity of LVF data bands and significant inter-band differences. Even after geometric processing, adjacent flight lines still exhibit varying degrees of geometric deformation. In this paper, a progressive grouping-based strategy for iterative band selection and registration is proposed. In addition, an improved Scale-Invariant Feature Transform (SIFT) algorithm, termed the Double Sufficiency–SIFT (DS-SIFT) algorithm, is introduced. This method first groups bands, selects the optimal reference band, and performs coarse registration based on the SIFT method. Subsequently, during the fine registration stage, it introduces an improved position/scale/orientation joint SIFT registration algorithm (IPSO-SIFT) that integrates partitioning and the principle of structural similarity. This algorithm iteratively refines registration based on the grouping results. Experimental data obtained from a self-developed and integrated LVF hyperspectral remote sensing system are utilized to verify the effectiveness of the proposed algorithm. A comparison with classical algorithms, such as SIFT and PSO-SIFT, demonstrates that the registration of LVF hyperspectral data using the proposed method achieves superior accuracy and efficiency.