Accumulated error elimination method of aerial array image stitching

被引:0
|
作者
Yue G. [1 ]
Sun W. [1 ]
Zhang X. [1 ]
Li T. [1 ]
机构
[1] College of Aviation Combat Service, Air Force Aviation University, Changchun
关键词
Average error; Coordinate fine-tuning; Error-free cumulative; Image stitching;
D O I
10.3788/IRLA20200529
中图分类号
学科分类号
摘要
Aiming at the problem of error accumulation in aerial array remote sensing image stitching algorithms, a method for eliminating cumulative errors in aerial array image stitching was proposed. First of all, the attitude information and position information of the aerial remote sensing platform were used to complete the projection transformation of each image and rough image stitching. The independence of image's error in stitching after each projection transformation was ensured. Thereby the error accumulation in image stitching was avoided. Then, the SIFT algorithm was used to extract the image matching pairs point in the overlapping area of the rough stitching adjacent images. According to the position difference between matching pairs of adjacent images, the principle of error sharing was adopted, and independent slightly adjust and coordinate fine-tuning were performed one by one. Until a certain accuracy was met, the accuracy of image stitching was improved and the accumulation of errors was avoided at the same time. Finally, the image quality was evaluated by combining with subjective evaluation (Compare the geographic correspondence between the corrected satellite image and the stitched image in this article) and the objective evaluation (in SSIM algorithm). The theoretical analysis and experimental results show that this method can effectively avoid the problem of error accumulation in aerial array remote sensing image stitching. Compared with the current main splicing methods, this method can maintain consistency with the two-dimensional coordinates of geographic space, and has good practical value and application prospects. © 2021, Editorial Board of Journal of Infrared and Laser Engineering. All right reserved.
引用
收藏
相关论文
共 13 条
  • [1] Wei L, Zhong Z, Lang C, Et al., A survey on image and video stitching, Virtual Reality & Intelligent Hardware, 1, 1, pp. 55-83, (2019)
  • [2] Feng R T, Du Q Y, Li X H, Et al., Robust registration for remote sensing images by combining and localizing feature and area-based methods, ISPRS Journal of Photogrammetry and Remote Sensing, 151, pp. 15-26, (2019)
  • [3] Xu Hongzhen, Li Shichao, Ji Yuhan, Et al., Panoramic camera image mosaic method based on feature points, Transactions of the Chinese Society for Agricultural Machinery, 50, S1, pp. 150-158, (2019)
  • [4] Cai Huaiyu, Wu Xiaoyu, Zhuo Liran, Et al., Fast SIFT image stitching algorithm combining edge detection, Infrared and Laser Engineering, 47, 11, (2018)
  • [5] Zhang Dexin, Ma Guangfu, Shao Xiaowei, Image mosaic for one aerial reconnaissance CCD camera based on object straight edge nodes, Infrared and Laser Engineering, 41, 1, pp. 234-238, (2012)
  • [6] Xu Qiuhui, A method of geometric correction and mosaic of unmanned aerial vehicle remote sensing image without ground control points, (2013)
  • [7] Zhang Weiping, Li Xiujuan, Yu Junfeng, Et al., Remote sensing image mosaic technology based on SURF algorithm in agriculture, EURASIP Journal on Image and Video Processing, (2018)
  • [8] Shao Ruizhe, Du Chun, Chen Hao, Et al., Fast anchor point matching for emergency UAV image stitching using position and pose information, Sensors, 20, 7, (2020)
  • [9] Pei Hongxing, Liu Jinda, Ge Jialong, Et al., A review on image mosaicing techniques [J], Journal of Zhengzhou University(Natural Science Edition), 51, 4, pp. 1-10, (2019)
  • [10] Pandey Achala, Pati Umesh C., Image mosaicing: A deeper insight, Image and Vision Computing, 89, pp. 236-257, (2019)