A Novel Radiometric Control Set Sample Selection Strategy for Relative Radiometric Normalization of Multitemporal Satellite Images

• Published in: IEEE transactions on geoscience and remote sensing Vol. 59; no. 3; pp. 2503 - 2519
• Main Authors: Moghimi, Armin, Mohammadzadeh, Ali, Celik, Turgay, Amani, Meisam
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automatic control; Change vector analysis (CVA); Datasets; Histograms; Image acquisition; Image sensors; Indexes; Landsat; Landsat satellites; Linear regression; multitemporal satellite images; Optimization; Outliers (statistics); Pixels; radiometric control set samples (RCSS); Radiometry; Regression analysis; relative radiometric normalization (RRN); Remote sensing; Root-mean-square errors; Satellite broadcasting; Satellite imagery; Satellites; Sensors; Spaceborne remote sensing; Vector analysis
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2020.2995394

This article presents a new relative radiometric normalization (RRN) method for multitemporal satellite images based on the automatic selection and multistep optimization of the radiometric control set samples (RCSS). A novel image-fusion strategy based on the fast local Laplacian filter is employed to generate a difference index using the complementary information extracted from the change vector analysis and absolute gradient difference of the bitemporal satellite images. The difference index is then segmented into changed and unchanged pixels using a fast level-set method. A novel local outlier method is then applied to the unchanged pixels of the bitemporal images to identify the initial RCSS, which are then scored by a novel unchanged purity index, and the histogram of the scores is used to produce the final RCSS. The RRN between the bitemporal images is achieved by adjusting the subject image to the reference image using orthogonal linear regression on the final RCSS. The proposed method is applied to seven different data sets comprised of bitemporal images acquired by various satellites, including Landsat TM/ETM+, Sentinel 2B, Worldview 2/3, and Aster. The experimental results show that the method outperforms the state-of-the-art RRN methods. It reduces the average root-mean-square error (RMSE) of the best baseline method (IR-MAD) by up to 32% considering all data sets.