A generalized tool for accurate and efficient image registration of UAV multi-lens multispectral cameras by N-SURF matching

Jyun Ping Jhan, Jiann Yeou Rau

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


The original multispectral (MS) images obtained from multi-lens multispectral cameras (MSCs) have significant misregistration errors, which require image registration for precise spectral measurement. However, due to the nonlinearity intensity differences among MS images, performing image matching is difficult to find sufficient correct matches (CMs) for image registration, and results in a complex coarse-to-fine solution. Based on the modification of speed-up robust feature (SURF), we proposed a normalized SURF (N-SURF) that can significantly increase the amount of CMs among different pairs of MS images and make one-step image registration possible. In this study, we first introduce N-SURF and adopt different MS datasets acquired from three representative MSCs (MCA-12, Altum, and Sequoia) to evaluate its matching ability. Meanwhile, we utilized three image transformation models - affine transform (AT), projective transform (PT), and an extended projective transform (EPT) to correct the misregistration errors of MSCs and evaluate their co-registration correctness. The results show that N-SURF can obtain 6-20 times more CMs than SURF and can successfully match all pairs of MS images, while SURF failed in the cases of significant spectral differences. Moreover, visual comparison, accuracy assessment, and residual analysis show that EPT can more accurately correct the viewpoint and lens distortion differences of MSCs than AT and PT, and it can obtain co-registration accuracy of 0.2-0.4 pixels. Subsequently, using the successful N-SURF matching and EPT model, we developed an automatic MS image registration tool that is suitable for various multilens MSCs.

Original languageEnglish
Article number9429928
Pages (from-to)6353-6362
Number of pages10
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Computers in Earth Sciences
  • Atmospheric Science


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