Image registration of the dual-channel spaceborne hyperspectral imager with motion compensation

被引：0
|
作者：
Zhao H. [1 ]
Zhang X. [1 ]
Jia G. [1 ]
Qiu X. [1 ]
Zhai L. [2 ]
机构：
[1] Key Laboratory of Education Ministry of Precision Opto-mechatromics Technology, School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing
[2] Chinese Academy of Surveying and Mapping, Beijing
来源：
Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering | 2021年 / 50卷 / 03期
关键词：
Hyperspectral imager; Image registration; Mismatch analysis; Motion compensation; Resampling;
D O I：
10.3788/IRLA20211022
中图分类号：
学科分类号：
摘要：
Latest generation of dual-channel spaceborne hyperspectral imager based on visible near infrared (VNIR) and short wave infrared (SWIR) uses the field slitter to separate VNIR and SWIR channels into several sub-fields, and each sub-field image has different ground area at the same time. When using motion compensation technology to improve signal to noise ratio of the instrument, the observation angles of each sub-field are different, which leads to more complicated mismatch of images and make it impossible to get the continuous VNIR-SWIR spectrum of the ground pixel. The rule of image distortion and dual-channel mismatch quantitatively was analyzed by Rigorous imaging model, and the registration scheme by using geometric orientation of each sub-field separately as well as the phase correlation method was proposed on this basis. Verification based on dual-channel spaceborne hyperspectral simulated data of Dongtianshan under motion compensation was performed. The result shows that registration accuracy of traditional scheme based on correlation of images reaches 3.9 pixel, which means the continuous VNIR-SWIR spectrum of the ground pixel is still unavailable. The registration accuracy of the scheme proposed by this paper reaches 0.3 pixel, and the reflectance spectrum overlap ratio error of the overlapping bands of VNIR and SWIR reduces from 41.5% to 1.2%. Copyright ©2021 Infrared and Laser Engineering. All rights reserved.
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  • [1] Marshall M, Thenkabail P., Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation, ISPRS Journal of Photogrammetry & Remote Sensing, 108, pp. 205-218, (2015)
  • [2] Guanter L, Kaufmann H, Segl K, Et al., The EnMAP spaceborne imaging spectroscopy mission for earth observation, Remote Sensing, 7, 7, pp. 8830-8857, (2015)
  • [3] Zhang L., Perspectives on Chinese developments in spaceborne imaging spectroscopy: What to expect in the next 5-10 years, Geoscience and Remote Sensing Symposium (IGARSS), (2016)
  • [4] Gao Ya, Zhou Jialin, Hou Xue, Et al., Registration algorithm for hyperspectral image based on Gaussian fitting, Infrared and Laser Engineering, 45, S2, (2016)
  • [5] Wang Yun, Study on systematic geometric correction and image registration of hyperspectral Images, (2011)
  • [6] Liu Zhiwen, Liu Dingsheng, Wei Jingbo, Rigorous geometric model of Tiangong-1 hyperspectral spectrometer, Journal of Remote Sensing, 18, S1, pp. 62-67, (2014)
  • [7] Wu Yinan, Li Guoning, Zhang Ke, Et al., Registration model based on homologous points tracking of space camera assembly imaging, Infrared and Laser Engineering, 45, 3, (2016)
  • [8] Wang Yueming, Jia Jianxin, He Zhiping, Et al., Key technologies of advanced hyperspectral imaging system, Journal of Remote Sensing, 20, 5, pp. 850-857, (2016)
  • [9] Jia G, Zhao H, Shang H., Pixel-size-varying method for simulation of remote sensing images, Journal of Applied Remote Sensing, 8, 1, (2014)
  • [10] Zhang Hongwei, Fan Xiang, Zhu Bin, Et al., Dual-band infrared image registration with the introduction of outliers rejection mechanism, Infrared and Laser Engineering, 44, S1, pp. 23-28, (2015)
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