ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 201–208, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-201-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 201–208, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-201-2020

  03 Aug 2020

03 Aug 2020

A COARSE-TO-FINE BAND REGISTRATION FRAMEWORK FOR MULTI/HYPERSPECTRAL REMOTE SENSING IMAGES CONSIDERING CLOUD INFLUENCE

X. Zhao1, Z. Gao1, W. Sun2, and F. Wen1 X. Zhao et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, 430079 Wuhan, China
  • 2School of Electronic Information, Wuhan University, 430079 Wuhan, China

Keywords: Multi/hyperspectral Band Registration, Moving Clouds, Coarse-to-fine Framework, RASL, Low-rank Analysis, Zhuhai-1 Satellite

Abstract. Band registration is one of the most critical steps in the production of multi/hyperspectral images and determines the accuracy of applications directly. Because of the characteristics of imaging devices in some multi/hyperspectral satellites, there may be a time difference between bands during push-broom imaging, which leads to displacements of moving clouds with respect to the ground. And a large number of feature points may gather around cloud contours due to the high contrast and rich texture, resulting in building a transformation more suitable for moving clouds and making ground objects ghosted and blurred. This brings a big challenge for registration methods based on feature extraction and matching. In this paper, we propose a novel coarse-to-fine band registration framework for multi/hyperspectral images containing moving clouds. In the coarse registration stage, a cloud mask is generated by grayscale stretching, morphology and other operations. Based on this mask, a coarse matching of cloud-free regions is performed to eliminate large misalignment between bands. In the refinement stage, low-rank analysis and RASL (Robust Alignment by Sparse and Low-rank decomposition) are used to optimize the rank of coarse results to achieve fine registration between bands. After experiments on a total of 102 images (83 cloudy images and 19 cloud-free images with all 32 bands) from Zhuhai-1 hyperspectral satellite, our method can achieve a registration accuracy of 0.6 pixels in cloudy images, 0.41 pixels in cloud-free images, which is enough for subsequent applications.