Light and Small UAV Hyperspectral Image Mosaicking
YI Li-na1, XU Xiao1, ZHANG Gui-feng2,3*, MING Xing2, GUO Wen-ji2, LI Shao-cong1, SHA Ling-yu1
1. China University of Mining & Technology, Beijing, Beijing 100083, China
2. Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract：The rapid development of small and low-cost unmanned aerial imaging spectrometer has provided new means for water quality monitoring and precision agriculture. The ZK-VNIR-FPG480 airborne hyperspectral imager is a domestic development instrument with independent property right. The image has a total of 270 bands, the spectral range is 400～1 000 nm, the spectral resolution is 3 nm, and the spatial resolution is 0.9 m@1 km. The imaging method is motion push broom imaging, which is characterized by no overlap between the images and only overlapping. While providing high spectral and high spatial resolution images, it also has a series of problems：①the narrow field of unmanned aerial vehicle (UAV) restricts the coverage of the ground surface of each airstrip, and requires the splicing of flights; ②the positioning accuracy of its POS system is low; ③In order to improve operational efficiency, the overlap ratio between navigation bands is relatively low, which is generally set at about 30%, making it difficult to image splicing; ④Due to the influence of wind, light, and the instrument itself during flight, there is a difference in brightness between each band, and stitching occurs when stitching occurs. This paper proposes a method for splicing UAV hyperspectral images based on surface spline function and phase correlation, aiming at solving the above problems. The aim is to splice a single hyperspectral band taken by UAV into a complete panorama with geographic coordinates, and to achieve image geometry and spectral matching. The method includes the following steps: First, the hyperspectral flight is georeferenced using the surface spline function method with the orthophoto image as the reference, and the real geographic coordinates of each flight are assigned; second, the local variance method is used to calculate the signal-to-noise ratio of each band, and the highest value band is taken as the optimal band; and then the phase correlation algorithm based on the 2 power image is used to correct the existing geographic spatial mapping relations between flights and eliminate the dislocation of the flight. Finally, the weighted average fusion method is used to fuse the adjacent flights and eliminate the problem of the mosaic line caused by the illumination and the instrument itself. Through the above steps, we can get a hyperspectral panorama with absolute geographic coordinates. The experiment uses ZK-VNIR-FPG480 airborne hyperspectral imager to get the hyperspectral data of a region of Dali to splice. The results show that the splicing method has no dislocations in the panorama stitching, and the geometric position is accurate. The curve directions of the 4 typical objects before and after splicing are basically the same. The average value of the spectral cosine of the left and right images before and after the stitching image was calculated to be 0.965 2, the average value of the spectral correlation coefficient was 0.863 2, the average value of the spectral information divergence was 0.424 0, and the average value of the Euclidean distance was 0.494 1. The four kinds of spectral curve similarity measure indicators objectively showed the high similarity of the curves, indicating that the spectral matching degree of the same name point before and after splicing is high, which is suitable for the splicing of UAV hyperspectral data. The method not only improves the accuracy of the geographical coordinates of the spliced image, but also ensures the maximum spectrum fidelity on the basis of the elimination of the joint joint. The 2 power image is introduced to solve the problem of the registration algorithm failure under the low overlap of the image. However, there is a spectral difference between the same name points in adjacent bands before splicing, and the amount of hyperspectral data is large and the splicing takes more time. It is still a problem to figure out how to use the pixels of the overlapped region to correct the system error, to unify the measurement space of the image, to improve the spectral accuracy and stability and to improve the stitching speed.
Key words：Hyperspectral image; Image mosaicking; Dcurved surface spline function; Signal to noise ratio; Phase correlation technique; Weighted average fusion
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