An Automated Approach for Sub-Pixel Registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery
An Automated Approach for Sub-Pixel Registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery
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DOI：
10.3390/rs8060520
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发表时间：
2016-06
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通讯作者：
Lin Yan;D. Roy;Hankui K. Zhang;Jian Li;Haiyan Huang
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文献类型：
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作者：
Lin Yan;D. Roy;Hankui K. Zhang;Jian Li;Haiyan Huang
来自Landsat-8 OLI和Sentinel-2AMSI传感器的中等空间分辨率卫星数据共同提供了10米至30米多光谱反射波长的全球覆盖，为改进传感器测绘和监测地球表面提供了机会。然而，标准的地理定位Landsat-8 OLI L1T和Sentinel-2AMSI L1C数据产品目前被发现未对准。提出了一种自动配准Landsat-8 OLi L1T和Sentinel-2AMSI L1C数据的方法，并使用同期的传感器数据进行了演示。该方法在计算上是高效的，因为它在四个图像金字塔层次上实现了特征点检测，以识别稀疏的连接点集。采用基于区域的最小二乘匹配方法，通过对图像金字塔各层进行失配检测，得到可靠的连接点。该方法通过检查提取的连接点空间分布和连接点映射变换(平移、仿射和二次多项式)、密集匹配预测误差评估和视觉配准评估来评估。选择了南非开普敦和林波波省的两个试验场，它们都有云和阴影。对16天和26天后感测的一幅Landsat-8 L1T图像和两幅Sentinel-2AL1C图像进行配准(开普敦)，以检验该算法对地表、大气和云层变化的稳健性，此外还对相隔4天感测的一对Landsat-8 L1T和Sentinel-2AL1C图像配准(林波波省)。自动提取的连接点显示，对于开普敦的两个图像对，传感器错误配准大于一个30 m Landsat-8像素尺寸，对于林波波图像对，大于一个10 m Sentinel-2A像素尺寸。变换拟合度评估表明，用仿射变换可以有效地刻画配准错误。提取了数百个自动定位的连接点，在10m分辨率下，仿射变换均方根误差约为0.3像素，密集匹配预测误差为相似量级。这些结果和对仿射变换数据的目视评估表明，该方法提供了有意义的Landsat-8 OLI和Sentinel-2AMSI数据比较和组合数据应用所需的亚像素配准性能。
Moderate spatial resolution satellite data from the Landsat-8 OLI and Sentinel-2A MSI sensors together offer 10 m to 30 m multi-spectral reflective wavelength global coverage, providing the opportunity for improved combined sensor mapping and monitoring of the Earth’s surface. However, the standard geolocated Landsat-8 OLI L1T and Sentinel-2A MSI L1C data products are currently found to be misaligned. An approach for automated registration of Landsat-8 OLI L1T and Sentinel-2A MSI L1C data is presented and demonstrated using contemporaneous sensor data. The approach is computationally efficient because it implements feature point detection across four image pyramid levels to identify a sparse set of tie-points. Area-based least squares matching around the feature points with mismatch detection across the image pyramid levels is undertaken to provide reliable tie-points. The approach was assessed by examination of extracted tie-point spatial distributions and tie-point mapping transformations (translation, affine and second order polynomial), dense-matching prediction-error assessment, and by visual registration assessment. Two test sites over Cape Town and Limpopo province in South Africa that contained cloud and shadows were selected. A Landsat-8 L1T image and two Sentinel-2A L1C images sensed 16 and 26 days later were registered (Cape Town) to examine the robustness of the algorithm to surface, atmosphere and cloud changes, in addition to the registration of a Landsat-8 L1T and Sentinel-2A L1C image pair sensed 4 days apart (Limpopo province). The automatically extracted tie-points revealed sensor misregistration greater than one 30 m Landsat-8 pixel dimension for the two Cape Town image pairs, and greater than one 10 m Sentinel-2A pixel dimension for the Limpopo image pair. Transformation fitting assessments showed that the misregistration can be effectively characterized by an affine transformation. Hundreds of automatically located tie-points were extracted and had affine-transformation root-mean-square error fits of approximately 0.3 pixels at 10 m resolution and dense-matching prediction errors of similar magnitude. These results and visual assessment of the affine transformed data indicate that the methodology provides sub-pixel registration performance required for meaningful Landsat-8 OLI and Sentinel-2A MSI data comparison and combined data applications.