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哨兵-2A影像的多光谱配准偏差：分析及其对潜在应用的影响

Multi-spectral misregistration of Sentinel-2A images: analysis and implications for potential applications.

作者信息

Skakun Sergii, Vermote Eric, Roger Jean-Claude, Justice Christopher

机构信息

Department of Geographical Sciences, University of Maryland, College Park, MD 20742 USA.

Terrestrial Information Systems Laboratory (Code 619), NASA Goddard Space Flight Center, Greenbelt, MD 20771 USA.

出版信息

IEEE Geosci Remote Sens Lett. 2017 Dec;14(12):2408-2412. doi: 10.1109/LGRS.2017.2766448. Epub 2017 Nov 10.

DOI:10.1109/LGRS.2017.2766448
PMID:29893382
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5992602/
Abstract

This study aims at analyzing sub-pixel misregistration between multi-spectral images acquired by the Multi-Spectral Instrument (MSI) aboard Sentinel-2A remote sensing satellite, and exploring its potential for moving target and cloud detection. By virtue of its hardware design, MSI's detectors exhibit a parallax angle that leads to sub-pixel shifts that are corrected with special pre-processing routines. However, these routines do not correct shifts for moving and/or high altitude objects. In this letter, we apply a phase correlation approach to detect sub-pixel shifts between B2 (blue), B3 (green) and B4 (red) Sentinel-2A/MSI images. We show that shifts of more than 1.1 pixels can be observed for moving targets, such as airplanes and clouds, and can be used for cloud detection. We demonstrate that the proposed approach can detect clouds that are not identified in the built-in cloud mask provided within the Sentinel-2A Level-1C (L1C) product.

摘要

本研究旨在分析哨兵 - 2A 遥感卫星搭载的多光谱仪器（MSI）获取的多光谱图像之间的亚像素配准误差，并探索其在移动目标和云检测方面的潜力。由于其硬件设计，MSI 的探测器存在视差角，会导致亚像素偏移，需通过特殊的预处理程序进行校正。然而，这些程序无法校正移动和/或高空物体的偏移。在本信函中，我们应用相位相关方法来检测哨兵 - 2A/MSI 的 B2（蓝色）、B3（绿色）和 B4（红色）图像之间的亚像素偏移。我们发现，对于飞机和云等移动目标，可观察到超过 1.1 像素的偏移，且可用于云检测。我们证明，所提出的方法能够检测出哨兵 - 2A 一级 C 级（L1C）产品中内置云掩码未识别出的云。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4e4/5992602/d0c7cee3ce32/nihms931297f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4e4/5992602/6c2a21931797/nihms931297f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4e4/5992602/3ad9123460fd/nihms931297f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4e4/5992602/3798150e6f2d/nihms931297f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4e4/5992602/d0c7cee3ce32/nihms931297f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4e4/5992602/6c2a21931797/nihms931297f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4e4/5992602/3ad9123460fd/nihms931297f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4e4/5992602/3798150e6f2d/nihms931297f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4e4/5992602/d0c7cee3ce32/nihms931297f4.jpg

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本文引用的文献

1
Automatic sub-pixel co-registration of Landsat-8 OLI and Sentinel-2A MSI images using phase correlation and machine learning based mapping.基于相位相关和机器学习映射的陆地卫星8号OLI和哨兵2A号MSI图像自动亚像素配准
Int J Digit Earth. 2017 Mar 23;Volume 10(Iss 12):1253-1269. doi: 10.1080/17538947.2017.1304586.
2
Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product.陆地卫星8号/OLI地表反射率产品性能的初步分析
Remote Sens Environ. 2016 Apr 28;Volume 185(Iss 2):46-56. doi: 10.1016/j.rse.2016.04.008.
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Extension of phase correlation to subpixel registration.
Sensors (Basel). 2018 Jun 29;18(7):2089. doi: 10.3390/s18072089.
将相位相关扩展到亚像素配准。
IEEE Trans Image Process. 2002;11(3):188-200. doi: 10.1109/83.988953.
4
Efficient subpixel image registration algorithms.高效亚像素图像配准算法。
Opt Lett. 2008 Jan 15;33(2):156-8. doi: 10.1364/ol.33.000156.