Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment

Zhang, Yujie; Mei, Xiaoguang; Ma, Yong; Jiang, Xingyu; Peng, Zongyi; Huang, Jun

doi:10.3390/rs14164038

Open AccessArticle

Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment

by

Yujie Zhang

,

Xiaoguang Mei

,

Yong Ma

,

Xingyu Jiang

,

Zongyi Peng

and

Jun Huang

^*

Electronic Information School, Wuhan University, Wuhan 430072, China

^*

Author to whom correspondence should be addressed.

Remote Sens. 2022, 14(16), 4038; https://doi.org/10.3390/rs14164038

Submission received: 29 July 2022 / Revised: 12 August 2022 / Accepted: 15 August 2022 / Published: 18 August 2022

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing: Methods and Applications)

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Abstract

Remote-sensing developments such as UAVs heighten the need for hyperspectral image stitching techniques that can obtain information on a large area through various parts of the same scene. State-of-the-art approaches often suffer from accumulation errors and high computational costs for large-scale hyperspectral remote-sensing images. In this study, we aim to generate high-precision hyperspectral panoramas with less spatial and spectral distortion. We introduce a new stitching strategy and apply it to hyperspectral images. The stitching framework was built as follows: First, a single band obtained by signal-to-noise ratio estimation was chosen as the reference band. Then, a feature-matching method combining the SuperPoint and LAF algorithms was adopted to strengthen the reliability of feature correspondences. Adaptive bundle adjustment was also designed to eliminate misaligned artifact areas and occasional accumulation errors. Lastly, a spectral correction method using covariance correspondences is proposed to ensure spectral consistency. Extensive feature-matching and image-stitching experiments on several hyperspectral datasets demonstrate the superiority of our approach over the state of the art.

Keywords: feature matching; hyperspectral images; image stitching

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MDPI and ACS Style

Zhang, Y.; Mei, X.; Ma, Y.; Jiang, X.; Peng, Z.; Huang, J. Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment. Remote Sens. 2022, 14, 4038. https://doi.org/10.3390/rs14164038

AMA Style

Zhang Y, Mei X, Ma Y, Jiang X, Peng Z, Huang J. Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment. Remote Sensing. 2022; 14(16):4038. https://doi.org/10.3390/rs14164038

Chicago/Turabian Style

Zhang, Yujie, Xiaoguang Mei, Yong Ma, Xingyu Jiang, Zongyi Peng, and Jun Huang. 2022. "Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment" Remote Sensing 14, no. 16: 4038. https://doi.org/10.3390/rs14164038

APA Style

Zhang, Y., Mei, X., Ma, Y., Jiang, X., Peng, Z., & Huang, J. (2022). Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment. Remote Sensing, 14(16), 4038. https://doi.org/10.3390/rs14164038

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Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment

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