Study on Radiometric Consistency of Landsat Images using Multi-band Joint Regression Model
DOI:
https://doi.org/10.54097/2qk21y18Keywords:
Radiometric Consistency, Landsat Images, Multi Satellite Sensors, Multi Temporal, Multi Band Joint Regression ModelAbstract
The long time-series dataset of radiometric consistency is the foundation of image processing, quantitative analysis, image fusion. However, due to the influence of atmospheric conditions, observation angles, geographical environment, reflective-band and other factors, the radiometric inconsistency exist in same-region Landsat images acquired by different satellites or acquired at different times. Therefore, this paper builds the radiation normalization model using Multi-band Joint Regression method, which is used to perform radiometric consistency conversion on Landsat 7/8 images. Meanwhile, the validity of the radiation normalization model is evaluated based on the validation data and vegetation index. Furthermore, the accuracy of the model is verified by comparing with the traditional single-band regression model. Finally, the experiment results show that the proposed model has better accuracy and can enhance the radiometric consistency of multi temporal Landsat images significantly.
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Huang Liting, Jiao Weili, et al. Radiometric Normalization of GF-1 Imagery Based on Regularized IR-MAD [J]. Remote Sensing Information. 2020, 35(3):11.
Jiang Sili, Huang W, et al. Thick cloud removal of remote sensing images based on multi-reference image information fusion [J]. Remote Sensing for Natural Resources, 2022, 34(2):121-127.
Cao Wenting. Long-term Continuous Monitoring of Island Urbanization and Its Ecological Consequences [D]. Zhejiang university, 2020.
CAO H, HAN L, LI L. Harmonizing surface reflectance between Landsat-7 ETM +, Landsat-8 OLI, and Sentinel-2 MSI over China [J]. Environ Sci Pollut Res Int, 2022.
LI P, JIANG L, FENG Z. Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors [J]. Remote Sensing, 2014, 6(1): 310-29.
Zhan Jun, Li Dan. Application of the Pseudo-invariant Feature Method for Image Relative Radiometric Normalization Process [J]. Geomatics & Spatial Information Technology, 2023(7):3.
Huang Qiting, Tan Zelin, et al. A Study on the Relative Radiometric Normalization of Multi-sources and Multi-temporal Remote Sensing Data [J]. Journal of Geo-information Science, 2016, 18(5): 606-614.
Bai Yang. Radiometric Normalization of Remote Sensing Image Based on Kernel Canonical Correlation Analysis [D]. The University of Chinese Academy of Sciences, 2018.
MANIVASAGAM V S, KAPLAN G, ROZENSTEIN O. Developing Transformation Functions for VENμS and Sentinel-2 Surface Reflectance over Israel [J]. Remote Sensing, 2019, 11(14).
Yu Xiaomin, Zhou Qin. Methods of Radiometric Normalization for Multi-Temporal Remote Sensing Images: A Review [J]. Geomatics & Spatial Information Technology, 2012 (6):5.
CHASTAIN R, HOUSMAN I, GOLDSTEIN J, et al. Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States [J]. Remote Sensing of Environment, 2019, 221: 274-85.
ROY D P, KOVALSKYY V, ZHANG H K, et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity [J]. Remote Sensing of Environment, 2016, 185: 57-70.
Xu Guangzhi, Xu Hanqiu. Cross-comparison of Sentinel-2A MSI and Landsat 8 OLI Multispectral Information [J]. Remote Sensing Technology and Application, 2021(1):11.
Wang Jingrui, Wei Xinyuan, et al. Assessing Relative Radiometric Normalization Performances of Surface Brightness Temperature from Multi-temporal Landsat Images [J]. Remote Sensing Information, 2016(2):7.
Xu feng, Li Hengkai. Cross-comparison of Landsat TM/OLI and HJ-1B CCD Sensor Data [J]. Remote Sensing Information, 2021(4):9.
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