A push-sweep hyperspectral aerial image Mosaic method based on SPHP
Author:
Affiliation:

1.Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China;2.CAS Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics,Shanghai 200083,China;3.University of Chinese Academy of Sciences,Beijing 100049,China;4.Shanghai Meteorological Bureau,Shanghai 200030,China

Clc Number:

P237

Fund Project:

Major special youth innovation fund project of high resolution earth observation system (NO: GFZX04061502);Research project of Shanghai water bureau (2018-07)

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    Abstract:

    In order to solve the problem that the optical center of hyperspectral images does not coincide with each other, and the alignment of traditional global monotonic matrix may have error in the registration process, a splicing-method (H-SPHP) of hyperspectral images based on SPHP(shape-preserving half-projective) and considering spectral information is proposed. The main steps include as follows: 1) image correction using the visual vector method. 2) selection of reference band based on prior knowledge and PCA. 3) SPHP-based mesh optimization splicing method. 4) Weighted average fusion algorithm for fusion.5) splicing parameters applied to all bands to obtain the splicing hyperspectral data. By obtaining experimental images from Sanming, Fujian province and Nanchang,Jiangxi province, the research data splicing experimental results show that the proposed algorithm has strong robustness, eliminating parallax and geographic coordinates accuracy better than SIFT + single should transform algorithm. After splicing, the registration accuracy of bands is within one pixel, and spectral similarity of the overlapping region is above 90%.

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LI Sai, Yin Qiu, HU Yong, GONG Cai-Lan. A push-sweep hyperspectral aerial image Mosaic method based on SPHP[J]. Journal of Infrared and Millimeter Waves,2021,40(1):64~73

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History
  • Received:January 17,2020
  • Revised:January 12,2021
  • Adopted:April 24,2020
  • Online: January 05,2021
  • Published: