# A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery
###### Abstract
This extraction method is robust against noise, geometric distortion, and radiometric differences, and has been extensively adopted for registration of optical imagery [16; 17; 18]. The SIFT descriptors were compared with steerable filters, differential invariants, complex filters, and moment invariants and performed best [19]. However, SIFT tends to obtain an insufficient number of matching point pairs and a low registration accuracy for multispectral images with intrinsic differences or images with illumination changes. For feature-based registration methods, robust fitting of global transformation models is very important. The RANdom SAmple Consensus (RANSAC) algorithm iteratively derives the parameters of a mathematic model from observation data that contains outliers [20; 21]. Based on RANSAC, improved methods such as maximum likelihood estimation sample consensus and progressive sample consensus were further developed [22; 23]. Due to their robustness and low computational complexity, the RANSAC and related methods were widely applied to outlier removal and fitting of transformation models for remote sensing image registration [24; 25]. In recent years, graph theory has been introduced to establish a geometrical relationship between feature points in image registration [26]. [PERSON] et al. first proposed a feature-graph-based point-matching algorithm that used local structural information [27]. Delaunay triangulation (DT) is an optimal planar partition that has been used to represent geometrical structures [28]. [PERSON] et al. adopted the graph structures from Delaunay triangulation based on the spatial triangulations among SIFT feature points. Moreover, they developed a novel dual-graph-based matching method for multispectral/multi-date image registration that significantly improved performance [29]. [PERSON] et al. proposed an accurate hyperspectral and infrared satellite image registration method that used the correspondences between local points (e.g., the Delaunay triangular edges and triangular surfaces) [30]. The constructed Delaunay triangular mesh enabled the estimation of transformation model parameters for each triangular facet, and was well adapted to the local distortion of satellite images. The flutter and focal plane distortion in Zhuhai-1 OHS imagery is caused by an unstable positioning accuracy which is a result of the limitations in complementary metal-oxide semiconductor (CMOS) technology. As a result, inconsistent geometric positioning between different spectral bands of hyperspectral images is created during image processing. [PERSON] et al. conducted on-orbit inspections of the external and internal parameter errors produced by the Zhuhai-1 hyperspectral camera, where Digital orthophoto map and digital elevation model data was used as control data. They established the internal orientation element model (IOEM) for on-orbit geometric calibration [31]. However, the registration accuracy produced by the IOEM method is easily affected by the satellite attitude stability. Additionally, the gray levels can vary for the same land features in different spectral bands, since the hyperspectral images are obtained by spectral imagers with gradient filters. This makes it problematic to extract and match the feature points of non-adjacent spectral bands in OHS hyperspectral images. Therefore, accurate inter-band registration of Zhuhai-1 satellite hyperspectral images remains challenging. In this paper, a novel full-spectrum registration algorithm was proposed based on feature point matching methods. The algorithm combines the transference strategy based on the affine transformation relationship between adjacent bands with the differential correction from dense Delaunay triangulation. The performance of the proposed registration method was evaluated using the coordinate deviation of matching points, as well as the similarity measures for hyperspectral images with different topographies and roll angles.