Radiometric normalization is an essential preprocessing step for almost all remote sensing applications such as change detection, image mosaic, and 3-D reconstruction. This article proposes a novel radiometric normalizing method based on spatiotemporal filtering using a reference moderate resolution imaging spectroradiometer (MODIS) product. This differs from traditional relative radiometric normalization (RRN) methods in two folds: first, the number of reference images is more than one, which introduces more complexities than RRN with a single reference image; second, the resolution of MODIS product is significantly lower, thus requiring the algorithms to accommodate scale differences. To address, our approach extends the traditional spatiotemporal filtering method with per image bias that represents both internal (e.g., sensor characteristics) and external (e.g., atmosphere and topography) against the reference data. In addition, we use the Kullback-Leibler divergence metric to statistically determine the resemblance degree between the temporal images for weighting. We applied our proposed method to normalize Landsat Operational Land Imager, Enhanced Thematic Mapper Plus +, and Sentinel MSI using MODIS Nadir BRDF-adjusted reflectance product, covering two study areas of 30 × 15 km ² and 32 × 52 km ² , respectively, and we show a notable radiometric consistency over both temporal and spatial dimension after the processing through three comparative experiments with state-of-the-art methods. 1) 3–7% improvement in the contexts of transfer learning, which favors only images with consistent radiometric properties and 2) Mosaic results using our processed images show no apparent seamlines as compared with images processed by other methods.

中文翻译：

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使用时空滤波使用参考MODIS产品对多时态Landsat和Sentinel-2图像进行辐射归一化

辐射归一化是几乎所有遥感应用（例如变化检测，图像镶嵌和3-D重建）必不可少的预处理步骤。本文提出了一种新的基于时空滤波的辐射归一化方法，该方法使用参考中分辨率成像光谱仪（MODIS）产品。这与传统的相对辐射归一化（RRN）方法有两个方面的区别：首先，参考图像的数量不止一个，比单个参考图像的RRN引入了更多的复杂性；其次，MODIS产品的分辨率明显较低，因此需要算法来适应规模差异。为了解决这一问题，我们的方法扩展了传统的时空滤波方法，每个图像偏差均代表了内部（例如，传感器特性）和外部（例如，大气和地形）参考数据。此外，我们使用Kullback-Leibler散度度量来统计确定时间图像之间的相似度以进行加权。我们使用MODIS Nadir BRDF调整后的反射率产品，使用我们提出的方法对Landsat操作性土地成像仪，Enhanced Thematic Mapper Plus +和Sentinel MSI进行了归一化，覆盖了两个30×15 km的研究区域 ^{分别通过2个}和32×52 km ² 进行处理，并且通过三个采用最新技术的比较实验进行处理后，我们在时间和空间维度上都显示出显着的辐射一致性。1）在迁移学习的背景下提高3–7％，这仅支持具有一致辐射特性的图像； 2）与其他方法处理的图像相比，使用我们处理的图像的镶嵌结果显示没有明显的接缝线。