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Improving Landsat Multispectral Scanner (MSS) geolocation by least-squares-adjustment based time-series co-registration
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.rse.2020.112181
L. Yan , D.P. Roy

Abstract The Landsat Multispectral Scanner (MSS) data sensed by the Landsat 1–5 satellites make up a significant portion of the early Landsat data record. However, accurate MSS image geolocation has been difficult to achieve systematically due to a number of factors associated primarily with the older sensor and satellite technology. As of August 2019, only 49% of the Landsat MSS archive could be processed at the highest-level L1TP (precision and terrain corrected) level, and the remainder were processed as L1GS (systematically corrected) with inaccurate geolocation and no terrain correction. This paper presents a methodology to improve the geolocation of MSS time series. The methodology uses an area- and feature-based least-squares matching scale-space algorithm, with a time series registration implementation, that we developed previously using Landsat-8 and Sentinel-2 imagery. The methodology requires that at least three L1TP images in the time series acquired over a given path/row are available. A linear combination of a polynomial transformation and multiple radially symmetric radial-basis-functions (RBFs) to model local uncorrected terrain relief effects present in the L1GS images are used. The processing is automated and applied in two passes. The first pass screens L1TP images to select the well-aligned ones that are used as references. The second pass registers the target images, including the L1GS images and any misaligned L1TP images, to all the reference L1TP images. The transformation coefficients for each registered target image are derived by least-squares adjustment using densely-matched tie-points between the target and the reference images. The methodology is demonstrated using 12 months of Landsat-4 MSS images at four Landsat path/row locations that contain agricultural, mountainous, and coastal regions, including a total of 43 L1TP and 31 L1GS images. There were sufficient tie-points to characterize the degree of misregistration of 14 L1GS images that had significant mean misregistration shifts ranging from 7.33 to 17.42 60 m pixels. In addition, at one site, two L1TP images were found to be misaligned and have mean misregistration shifts of 1.27 and 2.20 60 m pixels. The methodology provides sub-pixel registration accuracy - after registration, the mean misregistration shifts for the 14 L1GS and two misaligned L1TP images varied from only 0.10 to 0.41 60 m pixels. The methodology does not use a digital elevation model, and examples illustrate that although the RBF transformations can compensate terrain relief distortion effects, larger (~0.5 to 1.0 pixel) misregistration errors can remain in areas with highly variable terrain relief. Results are also provided for Landsat-1 MSS imagery to demonstrate the applicability of the methodology to even the earliest part of the Landsat record. Detailed qualitative and quantitative results are presented and indicate the potential of the methodology to improve the geolocation of the Landsat MSS data record that is discussed with recommendations for future research.

中文翻译:

通过基于最小二乘调整的时间序列联合配准改进 Landsat 多光谱扫描仪 (MSS) 地理定位

摘要 Landsat 1-5 卫星感知的 Landsat 多光谱扫描仪 (MSS) 数据构成了早期 Landsat 数据记录的重要部分。然而,由于主要与较旧的传感器和卫星技术相关的许多因素,难以系统地实现准确的 MSS 图像地理定位。截至 2019 年 8 月,只有 49% 的 Landsat MSS 档案可以在最高级别的 L1TP(精度和地形校正)级别进行处理,其余的作为 L1GS(系统校正)处理,具有不准确的地理定位和无地形校正。本文提出了一种改进 MSS 时间序列地理定位的方法。该方法使用基于面积和特征的最小二乘匹配尺度空间算法,以及时间序列配准实现,我们之前使用 Landsat-8 和 Sentinel-2 图像开发的。该方法要求在给定路径/行上获取的时间序列中至少有三个 L1TP 图像可用。使用多项式变换和多个径向对称径向基函数 (RBF) 的线性组合来模拟 L1GS 图像中存在的局部未校正地形起伏效应。该处理是自动化的并分两次应用。第一遍筛选 L1TP 图像以选择用作参考的对齐良好的图像。第二遍将目标图像(包括 L1GS 图像和任何未对齐的 L1TP 图像)配准到所有参考 L1TP 图像。每个注册目标图像的变换系数是通过使用目标和参考图像之间密集匹配的连接点进行最小二乘调整得出的。在包含农业、山区和沿海地区的四个 Landsat 路径/行位置使用 12 个月的 Landsat-4 MSS 图像展示了该方法,包括总共 43 个 L1TP 和 31 个 L1GS 图像。有足够的连接点来表征 14 幅 L1GS 图像的配准不当程度,这些图像的平均配准偏移范围从 7.33 到 17.42 60 m 像素不等。此外,在一个站点,发现两个 L1TP 图像未对齐,平均错位偏移为 1.27 和 2.20 60 m 像素。该方法提供亚像素配准精度 - 配准后,14 个 L1GS 和两个未对齐的 L1TP 图像的平均错位偏移仅从 0.10 到 0.41 60 m 像素不等。该方法不使用数字高程模型,示例表明,尽管 RBF 变换可以补偿地形起伏的失真效应,但在地形起伏变化很大的区域中,可能会保留较大(~0.5 到 1.0 像素)的配准误差。还提供了 Landsat-1 MSS 影像的结果,以证明该方法甚至适用于 Landsat 记录的最早部分。提供了详细的定性和定量结果,并表明该方法在改进 Landsat MSS 数据记录的地理定位方面的潜力,并与对未来研究的建议进行了讨论。该方法不使用数字高程模型,示例表明,尽管 RBF 变换可以补偿地形起伏的失真效应,但在地形起伏变化很大的区域中,可能会保留较大(~0.5 到 1.0 像素)的配准误差。还提供了 Landsat-1 MSS 影像的结果,以证明该方法甚至适用于 Landsat 记录的最早部分。提供了详细的定性和定量结果,并表明该方法在改进 Landsat MSS 数据记录的地理定位方面的潜力,并与对未来研究的建议进行了讨论。该方法不使用数字高程模型,示例表明,尽管 RBF 变换可以补偿地形起伏的失真效应,但在地形起伏变化很大的区域中,可能会保留较大(~0.5 到 1.0 像素)的配准误差。还提供了 Landsat-1 MSS 影像的结果,以证明该方法甚至适用于 Landsat 记录的最早部分。提供了详细的定性和定量结果,并表明该方法在改进 Landsat MSS 数据记录的地理定位方面的潜力,并与对未来研究的建议进行了讨论。0 像素)重合错误可能会保留在地形起伏变化很大的区域。还提供了 Landsat-1 MSS 影像的结果,以证明该方法甚至适用于 Landsat 记录的最早部分。提供了详细的定性和定量结果,并表明该方法在改进 Landsat MSS 数据记录的地理定位方面的潜力,并与对未来研究的建议进行了讨论。0 像素)配准错误可能会保留在地形起伏变化很大的区域。还提供了 Landsat-1 MSS 影像的结果,以证明该方法甚至适用于 Landsat 记录的最早部分。提供了详细的定性和定量结果,并表明该方法在改进 Landsat MSS 数据记录的地理定位方面的潜力,并与对未来研究的建议进行了讨论。
更新日期:2021-01-01
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