Abstract
Abstract. The accurate acquisition of land surface reflectance (SR) data determines the accuracy of ground objects recognition, classification and land surface parameter inversion using remote sensing data, which is the basis of remote sensing data application. In this study, a Control No-Changed Set (CNCS) radiometric normalization method is proposed to realize spectral information transformation of multi-sensor data, which is based on the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD), and includes automatic selection and step-by-step optimization of no-change pixels. The No-Changed set (NC) is obtained by selecting the original no-change pixels between the target image and the reference image according to the linear relationship. In the obtained original no-change regions, IR-MAD rules with iterative control are used to fix the final no-change pixels, after regression modeling and calculation, the normalized images are obtained. The method is tested on multi-images from multi-sensors in three groups of experiments (GF-1 WFV and Landsat-8 OLI, GF-1 PMS and Sentinel-2 MSI, and Landsat-8 OLI and Sentinel-2 MSI) with different landcover areas. The results of radiometric normalization are evaluated qualitatively and quantitatively. The data of the three groups of experiments have a high correlation (correlation coefficient r values > 0.85), indicating that they can be used together as complementary data. The Root Mean Squared Error (RMSE) values calculate from the NC between the reference and normalized target images are much smaller than those between the reference and original target images. The radiometric colour composition effects, and the typical ground objects spectral reflective curves of the reference and normalized target images are very similar after radiometric normalization. These results indicate that the CNCS method considers the linear relationship of the no-change pixels and is effective, stable, and can be used to improve the consistency of SR of multi-images from multi-sensors.
Highlights
The accurate acquisition of land surface reflectance (SR) data determines the accuracy of ground objects recognition (Zhang et al, 2015), land cover classification(Friedl et al, 2010) and land surface parameter inversion(Nazeer et al, 2017) using remote sensing data, which is the basis of remote sensing data application
This study proposes a Control No-Changed Set (CNCS) radiometric normalization method based on the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD)
The results show that the No-Changed set (NC) selection method is consistently effective
Summary
The accurate acquisition of land surface reflectance (SR) data determines the accuracy of ground objects recognition (Zhang et al, 2015), land cover classification(Friedl et al, 2010) and land surface parameter inversion(Nazeer et al, 2017) using remote sensing data, which is the basis of remote sensing data application. Zhou et al (2016) presented the utilization of normalized difference water index (NDWI) to select the original PIFs, and statistical rules with iterative control were used to fix the final PIFs. Recently, Syariz et al (2019) proposed a constrained orthogonal regression, a common radiometric level located between bitemporal images is selected as the reference, which enforces pixel spectral signatures to be as consistent as possible during radiometric normalization while band regression quality is preserved. Syariz et al (2019) proposed a constrained orthogonal regression, a common radiometric level located between bitemporal images is selected as the reference, which enforces pixel spectral signatures to be as consistent as possible during radiometric normalization while band regression quality is preserved These radiometric normalization methods do not involve the physical mechanism of remote sensing, and do not fully consider the influence factors such as atmosphere, so it is often impossible to obtain high-precision SR, which is not conducive to quantitative parameter inversion of multi-source remote sensing data
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