Consideration of Radiometric Quantization Error in Satellite Sensor Cross-Calibration

Bhatt, Rajendra; Doelling, David R.; Haney, Conor O.; Scarino, Benjamin

doi:10.3390/rs10071131

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…The assessment of uncertainty in SNO cross-calibration results involves the consideration of various factors, including measurement noise from the monitored instrument, calibration uncertainties from the reference instrument, data matching errors, and uncertainty in regression analysis, all of which require further investigation. Recently, the actual observation data of two specific satellites during the calibration were analyzed to assess the uncertainty of calibration results [20][21][22]. However, the quantification and analysis of uncertainty within the calibration process itself have not been extensively explored [23,24].…”

Section: Introductionmentioning

confidence: 99%

The Uncertainty of SNO Cross-Calibration for Satellite Infrared Channels

Chen

Dai

et al. 2023

Remote Sensing

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The on-orbit radiometric calibration is a fundamental task in quantitative remote sensing applications. A widely used calibration method is the cross-calibration based on Simultaneous Nadir Observation (SNO), which involves using high-precision reference instruments to calibrate lower-precision onboard instruments. However, despite efforts to match the observation time, spatial location, field geometry, and instrument spectra, errors can still be introduced during the matching processes and linear regression analysis. This paper focuses on the error generated by sample matching and the error fitting method generated by the sample fitting method. An error propagation analysis is performed to develop a generic model for assessing the uncertainty of the SNO cross-calibration method itself in meteorological satellite infrared channels. The model is validated using the payload parameters of the Hyperspectral Infrared Atmospheric Sounder (HIRAS) and the Medium Resolution Spectral Imager (MERSI) instruments aboard the FengYun-3D (FY-3D). Simulation experiments are performed considering typical bright temperatures, different background fields, and varying matching threshold conditions. The results demonstrate the effectiveness of the proposed model in capturing the error propagation chain in the SNO cross-calibration process. The model provides valuable insight into error analysis in the SNO cross-calibration method and can assist in determining the optimal sample matching threshold for achieving radiometric calibration accuracy.

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Section: Introductionmentioning

confidence: 99%

The Uncertainty of SNO Cross-Calibration for Satellite Infrared Channels

Chen

Dai

et al. 2023

Remote Sensing

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“…Digital Object Identifier 10.1109/JSTARS.2022.3176141 the clouds and the Earth's radiant energy system record (2000 to the present), most GEO imagers have implemented 10-b quantization [7]. However, in existing convolutional neural network (CNN)based ship detection methods, the training samples are generally 8-b images.…”

Section: Introductionmentioning

confidence: 99%

LMO-YOLO: A Ship Detection Model for Low-Resolution Optical Satellite Imagery

Zheng

Shi

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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It has been observed that the existing convolutional neural network (CNN)-based ship detection models often result in high false detection rate in low-resolution optical satellite images. This problem arises from the following factors: 1) the current 8-b rescaling schemes make the images lose some important information about ships in low-resolution imagery; 2) the effective features of ships at low resolution are far fewer than those of ships at high resolution; and 3) the detection of low-resolution ships is more sensitive to object-background contrast variation. To solve these problems, a low-resolution marine object (LMO) detection YOLO model, called LMO-YOLO, is proposed in this article. First, a multiple linear rescaling scheme is developed to quantize the original satellite images into 8-b images; second, dilated convolutions are included in a YOLO network to extract object features and object-background features; finally, an adaptive weighting scheme is designed to balance the loss between easy-to-detect ships and hard-to-detect ships. The proposed method was validated by level 1 product images captured by the wide-field-of-view sensor on the GaoFen-1 satellite. The experimental results demonstrated that our method accurately detected ships from low-resolution images and outperformed state-of-the-art methods.

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“…As a result, because the Landsat-8 OLI and Sentinel-2 sensors are both polar-orbiting and sun-synchronous, they can provide medium spatial resolution satellite data, allowing for better mapping and monitoring of the Earth’s surface (Li and Roy, 2017; Kosari et al , 2020). For the performance of vicarious calibration, the natural or artificial sites on the Earth’s surface were used for calibration of satellite sensors (Biggar et al , 2003; Cao et al , 2004), such as vicarious calibration of the HJ-A CCD-1 sensors (Zhong et al , 2014), Beijing-1 multispectral imagers (Chen et al , 2014) and FY-3 (Yang et al , 2017), and also cross-calibration of GMS-5 (Bhatt et al , 2018) and ASTER (Obata et al , 2015). Many studies have been performed on the radiometric calibration of the Sentinel-2 sensor (Main-Knorn et al , 2017; Chastain et al , 2019; Pahlevan et al , 2019; Revel et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

A fast radiometric correction method for Sentinel-2 satellite images

Moradi

Sharifi

2021

AEAT

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Purpose Radiometric calibration is a method that estimates the reflection of the target from the measured input radiation. The purpose of this study is to radiometrically calibrate three spectral bands of Sentinel-2A, including green, red and infrared. For this purpose, Landsat-8 OLI data are used. Because they have bands with the same wavelength range and they have the same structure. As a result, Landsat-8 OLI is appropriate for relative radiometric calibration. Design/methodology/approach The method used in this study is radiometric calibration uncorrected data from a sensor with corrected data from another sensor. Also, another aim of this study is a comparison between radiometric correction data and data that, in addition to radiometric correction, has been sharpened with panchromatic data. In this method, both of them have been used for radiometric calibration. Calibration coefficients have been obtained using the first-order polynomial equation. Findings This study showed that the corrected data has more valid answers than corrected and sharpened data. This method studied three land-cover types, including soil, water and vegetation, which it obtained the most accurate coefficients of calibration for soil class because R-square in all three bands was above 88%, and the root mean square error in all three bands was below 0.01. In the case of water and vegetation classes, only results of red and infrared bands were suitable. Originality/value For validating this method, the radiometric correction module of SNAP software was used. According to the results, the coefficient of radiometric calibration of the Landsat-8 sensor was very close to the coefficients obtained from the corrected data by SNAP.

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Consideration of Radiometric Quantization Error in Satellite Sensor Cross-Calibration

Cited by 5 publications

References 28 publications

The Uncertainty of SNO Cross-Calibration for Satellite Infrared Channels

The Uncertainty of SNO Cross-Calibration for Satellite Infrared Channels

LMO-YOLO: A Ship Detection Model for Low-Resolution Optical Satellite Imagery

A fast radiometric correction method for Sentinel-2 satellite images

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