Radiometric Normalization of Multitemporal Landsat and Sentinel-2 Images Using a Reference MODIS Product Through Spatiotemporal Filtering
| Autor: | Rongjun Qin, Wenxia Gan, Hessah Albanwan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Remote sensing application Computer science Reference data (financial markets) Geophysics. Cosmic physics 0211 other engineering and technologies Kullback-Leibler (KL) divergence 02 engineering and technology Bias term 01 natural sciences Nadir Computers in Earth Sciences intersensor normalization TC1501-1800 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Landsat 7 Enhanced Thematic Mapper Plus (ETM+) QC801-809 Landsat 8 Operational Land Imager (OLI) Weighting Ocean engineering Thematic Mapper Radiometry Moderate-resolution imaging spectroradiometer Change detection radiometric consistency |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 4000-4013 (2021) |
| ISSN: | 2151-1535 |
| Popis: | 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 km2 and 32 × 52 km2, 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. |
| Databáze: | OpenAIRE |
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