# A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery
###### Abstract  
Accurate registration is an essential prerequisite for analysis and applications involving remote sensing imagery. It is usually difficult to extract enough matching points for inter-band registration in hyperspectral imagery due to the different spectral responses for land features in different image bands. This is especially true for non-adjacent bands. The inconsistency in geometric distortion caused by topographic relief also makes it inappropriate to use a single affine transformation relationship for the geometric transformation of the entire image. Currently, accurate registration between spectral bands of Zhuhai-1 satellite hyperspectral imagery remains challenging. In this paper, a full-spectrum registration method was proposed to address this problem. The method combines the transfer strategy based on the affine transformation relationship between adjacent spectrums with the differential correction from dense Delaunay triangulation. Firstly, the scale-invariant feature transform (SIFT) extraction method was used to extract and match feature points of adjacent bands. The RANdom SAmple Consensus (RANSAC) algorithm and the least square method is then used to eliminate mismatching point pairs to obtain fine matching point pairs. Secondly, a dense Delaunay triangulation was constructed based on fine matching point pairs. The affine transformation relation for non-adjacent bands was established for each triangle using the affine transformation relation transfer strategy. Finally, the affine transformation relation was used to perform differential correction for each triangle. Three Zhuhai-1 satellite hyperspectral images covering different terrains were used as experiment data. The evaluation results showed that the adjacent band registration accuracy ranged from 0.2 to 0.6 pixels. The structural similarity measure and cosine similarity measure between non-adjacent bands were both greater than 0.80. Moreover, the full-spectrum registration accuracy was less than 1 pixel. These registration results can meet the needs of Zhuhai-1 hyperspectral imagery applications in various fields. Zhuhai-1 satellite; hyperspectral image; scale-invariant feature transform; full-spectrum registration; Delaunay triangulation 20202  
including video, hyperspectral, radar, high-resolution optical, and infrared satellites in different orbits. A total of twelve satellites have been launched so far and include four video satellites (Orbita Video Satellites (OVS)) and eight hyperspectral satellites (Orbita Hyperspectral Satellites (OHS)). These eight hyperspectral satellites can observe the Earth's surface every 2.5 days and have become the world's leading hyperspectral satellite constellation. The satellites' hyperspectral imagery has been widely used in land and resource mapping, water quality monitoring, precision agriculture services, and other fields [1]. Image registration is an indispensable prerequisite for image analysis processes such as geometric correction, image fusion, and image stitching [2; 3; 4]. It is also an important initial step for remote sensing applications in fields such as change detection, environmental monitoring, crop yield estimation, mineral exploration, and so on [5; 6; 7; 8; 9]. The various image registration methods can be divided into two categories, including area-based registration and feature-based registration [10; 11]. The area-based registration methods are mostly implemented by analyzing the grayscale distribution and histogram of images [12; 13]. Their limitation lies in the difficulty in processing images with different grayscale characteristics. The feature-based registration method estimates the mapping relationship by extracting features such as feature points, lines, or edges, and the morphological regions of two images. An advantage of feature-based methods is their capability for handling the complex misalignments between scenes [14; 15]. The scale-invariance feature transform (SIFT) method allows for the extraction of distinctive invariant features from images.