scholarly journals Extended Pseudo Invariant Calibration Sites (EPICS) for the Cross-Calibration of Optical Satellite Sensors

2019 ◽  
Vol 11 (14) ◽  
pp. 1676 ◽  
Author(s):  
Mahesh Shrestha ◽  
Md. Nahid Hasan ◽  
Larry Leigh ◽  
Dennis Helder
Keyword(s):  
Region Of Interest ◽  
Image Data ◽  
Calibration Data ◽  
Gain Ratio ◽  
Time Period ◽  
Satellite Sensors ◽  
The Cross ◽  
Cross Calibration ◽  
Similar Accuracy ◽  
Shortwave Infrared

An increasing number of Earth-observing satellite sensors are being launched to meet the insatiable demand for timely and accurate data to aid the understanding of the Earth’s complex systems and to monitor significant changes to them. To make full use of the data from these sensors, it is mandatory to bring them to a common radiometric scale through a cross-calibration approach. Commonly, cross-calibration data were acquired from selected pseudo-invariant calibration sites (PICS), located primarily throughout the Saharan desert in North Africa, determined to be temporally, spatially, and spectrally stable. The major limitation to this approach is that long periods of time are required to assemble sufficiently sampled cloud-free cross-calibration datasets. Recently, Shrestha et al. identified extended, cluster-based sites potentially suitable for PICS-based cross-calibration and estimated representative hyperspectral profiles for them. This work investigates the performance of extended pseudo-invariant calibration sites (EPICS) in cross-calibration for one of Shrestha’s clusters, Cluster 13, by comparing its results to those obtained from a traditional PICS-based cross-calibration. The use of EPICS clusters can significantly increase the number of cross-calibration opportunities within a much shorter time period. The cross-calibration gain ratio estimated using a cluster-based approach had a similar accuracy to the cross-calibration gain derived from region of interest (ROI)-based approaches. The cluster-based cross-calibration gain ratio is consistent within approximately 2% of the ROI-based cross-calibration gain ratio for all bands except for the coastal and shortwave-infrared (SWIR) 2 bands. These results show that image data from any region within Cluster 13 can be used for sensor cross-calibration.

scholarly journals Hyperspectral Empirical Absolute Calibration Model Using Libya 4 Pseudo Invariant Calibration Site

2021 ◽  
Vol 13 (8) ◽  
pp. 1538
Author(s):  
Manisha Das Chaity ◽  
Morakot Kaewmanee ◽  
Larry Leigh ◽  
Cibele Teixeira Teixeira Pinto
Keyword(s):  
Zenith Angle ◽  
Spectral Resolution ◽  
Region Of Interest ◽  
Image Data ◽  
Absolute Calibration ◽  
Landsat 8 ◽  
Calibration Model ◽  
View Zenith Angle ◽  
Satellite Sensors ◽  
Terra Modis

The objective of this paper is to find an empirical hyperspectral absolute calibration model using Libya 4 pseudo invariant calibration site (PICS). The approach involves using the Landsat 8 (L8) Operational Land Imager (OLI) as the reference radiometer and using Earth Observing One (EO-1) Hyperion, with a spectral resolution of 10 nm as a hyperspectral source. This model utilizes data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the Libya 4 PICS. It uses an improved, simple, empirical, hyperspectral Bidirectional Reflectance Distribution function (BRDF) model accounting for four angles: solar zenith and azimuth, and view zenith and azimuth angles. This model can perform absolute calibration in 1 nm spectral resolution by predicting TOA reflectance in all existing spectral bands of the sensors. The resultant model was validated with image data acquired from satellite sensors such as Landsat 7, Sentinel 2A, and Sentinel 2B, Terra MODIS, Aqua MODIS, from their launch date to 2020. These satellite sensors differ in terms of the width of their spectral bandpass, overpass time, off-nadir viewing capabilities, spatial resolution, and temporal revisit time, etc. The result demonstrates the efficacy of the proposed model has an accuracy of the order of 3% with a precision of about 3% for the nadir viewing sensors (with view zenith angle up to 5) used in the study. For the off-nadir viewing satellites with view zenith angle up to 20, it can have an estimated accuracy of 6% and precision of 4%.

scholarly journals Radiometric Cross-calibration of KOMPSAT-3A with Landsat-8

2016 ◽  
Vol XLI-B1 ◽  
pp. 379-382
Author(s):  
D. Y. Shin ◽  
H. Y. Ahn ◽  
S. G. Lee ◽  
C. U. Choi ◽  
J. S. Kim
Keyword(s):  
Optical Quality ◽  
Calibration Coefficient ◽  
Spectral Radiance ◽  
Color Filter ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Atmospheric Conditions ◽  
Satellite Sensors ◽  
The Cross ◽  
Cross Calibration

In this study, Cross calibration was conducted at the Libya 4 PICS site on 2015 using Landsat-8 and KOMPSAT-3A. Ideally a cross calibration should be calculated for each individual scene pair because on any given date the TOA spectral profile is influenced by sun and satellite view geometry and the atmospheric conditions. However, using the near-simultaneous images minimizes this effect because the sensors are viewing the same atmosphere. For the cross calibration, the calibration coefficient was calculated by comparing the at sensor spectral radiance for the same location calculated using the Landsat-8 calibration parameters in metadata and the DN of KOMPSAT-3A for the regions of interest (ROI). Cross calibration can be conducted because the satellite sensors used for overpass have a similar bandwidth. However, not all satellites have the same color filter transmittance and sensor reactivity, even though the purpose is to observe the visible bands. Therefore, the differences in the RSR should be corrected. For the cross-calibration, a calibration coefficient was calculated using the TOA radiance and KOMPSAT-3 DN of the Landsat-8 OLI overpassed at the Libya 4 Site, As a result, the accuracy of the calibration coefficient at the site was assumed to be ± 1.0%. In terms of the results, the radiometric calibration coefficients suggested here are thought to be useful for maintaining the optical quality of the KOMPSAT-3A.

scholarly journals Radiometric Cross-calibration of KOMPSAT-3A with Landsat-8

2016 ◽  
Vol XLI-B1 ◽  
pp. 379-382
Author(s):  
D. Y. Shin ◽  
H. Y. Ahn ◽  
S. G. Lee ◽  
C. U. Choi ◽  
J. S. Kim
Keyword(s):  
Optical Quality ◽  
Calibration Coefficient ◽  
Spectral Radiance ◽  
Color Filter ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Atmospheric Conditions ◽  
Satellite Sensors ◽  
The Cross ◽  
Cross Calibration

In this study, Cross calibration was conducted at the Libya 4 PICS site on 2015 using Landsat-8 and KOMPSAT-3A. Ideally a cross calibration should be calculated for each individual scene pair because on any given date the TOA spectral profile is influenced by sun and satellite view geometry and the atmospheric conditions. However, using the near-simultaneous images minimizes this effect because the sensors are viewing the same atmosphere. For the cross calibration, the calibration coefficient was calculated by comparing the at sensor spectral radiance for the same location calculated using the Landsat-8 calibration parameters in metadata and the DN of KOMPSAT-3A for the regions of interest (ROI). Cross calibration can be conducted because the satellite sensors used for overpass have a similar bandwidth. However, not all satellites have the same color filter transmittance and sensor reactivity, even though the purpose is to observe the visible bands. Therefore, the differences in the RSR should be corrected. For the cross-calibration, a calibration coefficient was calculated using the TOA radiance and KOMPSAT-3 DN of the Landsat-8 OLI overpassed at the Libya 4 Site, As a result, the accuracy of the calibration coefficient at the site was assumed to be ± 1.0%. In terms of the results, the radiometric calibration coefficients suggested here are thought to be useful for maintaining the optical quality of the KOMPSAT-3A.

scholarly journals Radiometric Cross Calibration and Validation Using 4 Angle BRDF Model between Landsat 8 and Sentinel 2A

2020 ◽  
Vol 12 (5) ◽  
pp. 806 ◽  
Author(s):  
M M Farhad ◽  
Morakot Kaewmanee ◽  
Larry Leigh ◽  
Dennis Helder
Keyword(s):  
Spectral Band ◽  
Image Data ◽  
Calibration Procedure ◽  
Landsat 8 ◽  
Blue Band ◽  
Calibration Uncertainty ◽  
Significant Difference ◽  
The Cross ◽  
Sentinel 2A ◽  
Cross Calibration

This work describes a proposed radiometric cross calibration between the Landsat 8 Operational Land Imager (OLI) and Sentinel 2A Multispectral Instrument (MSI) sensors. The cross-calibration procedure involves (i) correction of the MSI data to account for spectral band differences with OLI and (ii) normalization of Bidirectional Reflectance Distribution Function (BRDF) effects in the data from both sensors using a new model accounting for the view zenith/azimuth angles in addition to the solar zenith/view angles. Following application of the spectral and BRDF normalization, standard least-squares linear regression is used to determine the cross-calibration gain and offset in each band. Uncertainties related to each step in the proposed process are determined, as is the overall uncertainty associated with the complete processing sequence. Validation of the proposed cross-calibration gains and offsets is performed on image data acquired over the Algodones Dunes site. The results of this work indicate that the blue band has the most significant offset, requiring use of the estimated cross-calibration offset in addition to the estimated gain. The highest difference was observed in the blue and red bands, which are 2.6% and 1.4%, respectively, while other bands shows no significant difference. Overall, the net uncertainty in the proposed process was estimated to be on the order of 6.76%, with the largest uncertainty component due to each sensor’s calibration uncertainty on the order of 5% and 3% for the MSI and OLI, respectively. Other significant contributions to the uncertainty include seasonal changes in solar zenith and azimuth angles, target site nonuniformity, variability in atmospheric water vapor, and/or aerosol concentration.

scholarly journals Combination of Cross- and Inter-Band Radiometric Calibrations for a Hyperspectral Sensor Using Model-Based Spectral Band Adjustment

2020 ◽  
Vol 12 (12) ◽  
pp. 2011
Author(s):  
Hiroki Mizuochi ◽  
Satoshi Tsuchida ◽  
Kenta Obata ◽  
Hirokazu Yamamoto ◽  
Satoru Yamamoto
Keyword(s):  
Near Infrared ◽  
Spectral Band ◽  
Space Station ◽  
Infrared Region ◽  
Band Adjustment ◽  
Moderate Resolution ◽  
Satellite Sensors ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cross Calibration ◽  
Shortwave Infrared

Recently, the growing number of hyperspectral satellite sensors have increased the demand for a flexible and robust approach to their calibration. This paper proposes an operational method for the simultaneous correction of inter-sensor and inter-band biases in hyperspectral sensors via the soil line concept for spectral band adjustment. Earth Observing-1 Hyperion was selected as an example, with the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference. The results over the Railroad Valley Playa calibration site indicated that the discrepancy in the analogous bands between Hyperion and MODIS during 2001–2008 was approximately 4–6% and 7–9% of the root-mean-square error in the top-of-atmosphere (TOA) radiance at the visible and near-infrared region and shortwave infrared region, respectively. For all Hyperion bands, the relative cross-calibration coefficients during this period were calculated (typically ranging from 0.9 to 1.1) to correct the Hyperion TOA radiance to be consistent with the MODIS and the other Hyperion bands. The application of the proposed approach could allow for more flexible cross-calibration of irregular-orbit sensors aboard the International Space Station.

scholarly journals Extended Pseudo Invariant Calibration Site-Based Trend-to-Trend Cross-Calibration of Optical Satellite Sensors

2021 ◽  
Vol 13 (8) ◽  
pp. 1545
Author(s):  
Prathana Khakurel ◽  
Larry Leigh ◽  
Morakot Kaewmanee ◽  
Cibele Teixeira Pinto
Keyword(s):  
Radiometric Calibration ◽  
The North ◽  
Landsat 7 ◽  
Order Of Magnitude ◽  
Satellite Sensors ◽  
The Cross ◽  
Sentinel 2A ◽  
Cross Calibration ◽  
Traditional Approaches ◽  
Short Time

Satellite sensors have been extremely useful and are in massive demand in the understanding of the Earth’s surface and monitoring of changes. For quantitative analysis and acquiring consistent measurements, absolute radiometric calibration is necessary. The most common vicarious approach of radiometric calibration is cross-calibration, which helps to tie all the sensors to a common radiometric scale for consistent measurement. One of the traditional methods of cross-calibration is performed using temporally and spectrally stable pseudo-invariant calibration sites (PICS). This technique is limited by adequate cloud-free acquisitions for cross-calibration which would require a longer time to study the differences in sensor measurements. To address the limitation of traditional PICS-based approaches and to increase the cross-calibration opportunity for quickly achieving high-quality results, the approach presented here is based on using extended pseudo invariant calibration sites (EPICS) over North Africa. With the EPICS-based approach, the area of extent of the cross-calibration site covers a large portion of the North African continent. With targets this large, many sensors should image some portion of EPICS nearlydaily, allowing for evaluation of performance with much greater frequency. By using these near-daily measurements, trends of the sensor’s performance are then used to evaluate sensor-to-sensor daily cross-calibration. With the use of the proposed methodology, the dataset for cross-calibration is increased by an order of magnitude compared to traditional approaches, resulting in the differences between any two sensors being detected within a much shorter time. Using this new trend in trend cross-calibration approaches, gains were evaluated for Landsat 7/8 and Sentinel 2A/B, with the results showing that the sensors are calibrated within 2.5% (within less than 8% uncertainty) or better for all sensor pairs, which is consistent with what the traditional PICS-based approach detects. The proposed cross-calibration technique is useful to cross-calibrate any two sensors without the requirement of any coincident or near-coincident scene pairs, while still achieving results similar to traditional approaches in a short time.

scholarly journals Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations

2020 ◽  
Vol 12 (3) ◽  
pp. 427 ◽  
Author(s):  
Satoshi Tsuchida ◽  
Hirokazu Yamamoto ◽  
Toru Kouyama ◽  
Kenta Obata ◽  
Fumihiro Sakuma ◽  
...  
Keyword(s):  
Near Infrared ◽  
Thermal Emission ◽  
Thermal Infrared ◽  
Vicarious Calibration ◽  
The Cross ◽  
Sensor Degradation ◽  
Cross Calibration ◽  
Shortwave Infrared ◽  
Infrared Radiometer

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard Terra platform, which was launched in 1999, has three separate subsystems: a visible and near-infrared (VNIR) radiometer, a shortwave-infrared radiometer, and a thermal-infrared radiometer. The ASTER VNIR bands have been radiometrically corrected for approximately 14 years by the sensor degradation curves estimated from the onboard calibrator according to the original calibration plan. However, this calibration by the onboard calibrator encountered a problem; specifically, it is inconsistent with the results of vicarious calibration and cross calibration. Therefore, the ASTER VNIR processing was applied by the radiometric degradation curves calculated from the results of three calibration approaches, i.e., the onboard calibrator, the vicarious calibration, and the cross calibration since February 2014. Even though the current degradation curves were revised, the inter-band and lunar calibrations show some inconsistencies owing to the different traceability in the bands by different calibration approaches. In this study, the current degradation curves and their problems are explained, and the new curves that are derived from the vicarious calibration with lunar calibration are discussed. The new degradation curves that have the same traceability in the bands will be used for future ASTER VNIR processing.

Automated Diagonal Slice and View Solution for 3D Device Structure Analysis

2018 ◽  
Author(s):  
Sang Hoon Lee ◽  
Jeff Blackwood ◽  
Stacey Stone ◽  
Michael Schmidt ◽  
Mark Williamson ◽  
...  
Keyword(s):  
Cross Section ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Image Data ◽  
Planar Surface ◽  
Device Structure ◽  
Cross Sectional ◽  
Device Structures ◽  
The Cross ◽  
Planar Analysis

Abstract The cross-sectional and planar analysis of current generation 3D device structures can be analyzed using a single Focused Ion Beam (FIB) mill. This is achieved using a diagonal milling technique that exposes a multilayer planar surface as well as the cross-section. this provides image data allowing for an efficient method to monitor the fabrication process and find device design errors. This process saves tremendous sample-to-data time, decreasing it from days to hours while still providing precise defect and structure data.

Cross Calibration by FFT Equalization

1997 ◽  
Vol 119 (2) ◽  
pp. 236-242 ◽  
Author(s):  
K. Peleg
Keyword(s):  
Measurement Method ◽  
Functional Relationship ◽  
Measurement Methods ◽  
New Method ◽  
Measurement Systems ◽  
True Value ◽  
Calibration Problem ◽  
Regression Techniques ◽  
The Cross ◽  
Cross Calibration

The classical calibration problem is primarily concerned with comparing an approximate measurement method with a very precise one. Frequently, both measurement methods are very noisy, so we cannot regard either method as giving the true value of the quantity being measured. Sometimes, it is desired to replace a destructive or slow measurement method, by a noninvasive, faster or less expensive one. The simplest solution is to cross calibrate one measurement method in terms of the other. The common practice is to use regression models, as cross calibration formulas. However, such models do not attempt to discriminate between the clutter and the true functional relationship between the cross calibrated measurement methods. A new approach is proposed, based on minimizing the sum of squares of the differences between the absolute values of the Fast Fourier Transform (FFT) series, derived from the readings of the cross calibrated measurement methods. The line taken is illustrated by cross calibration examples of simulated linear and nonlinear measurement systems, with various levels of additive noise, wherein the new method is compared to the classical regression techniques. It is shown, that the new method can discover better the true functional relationship between two measurement systems, which is occluded by the noise.

scholarly journals Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5549
Author(s):  
Ossi Kaltiokallio ◽  
Roland Hostettler ◽  
Hüseyin Yiğitler ◽  
Mikko Valkama
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Tracking System ◽  
Model Parameters ◽  
Calibration Data ◽  
Tracking Accuracy ◽  
Time Period ◽  
The Em Algorithm ◽  
Device Free ◽  
Localization And Tracking

Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm’s potential, a novel localization-and-tracking system is presented to estimate a target’s arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.

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