On-orbit characterization of the GOES Imager channel-to-channel co-registration

Grotenhuis, M.; Wu, Xiangqian; Yu, Fangfang; Schmit, Timothy J.; Lindstrom, Scott B.; Cao, Changyong

doi:10.1117/12.929590

Cited by 3 publications

(9 citation statements)

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“…A study by Cooperative Institute for Meteorological Satellite Studies personnel, as well as Grotenhuis et al, 2 found that the co-registration error between channels 2 (3.9 μm) and 4 (10.7 μm) could be as large as 1 pixel in the east/west direction (or ∼4 km), which is considerably larger than the instrument specification. Characterization of GOES channel to channel coregistration errors was seen as an important problem to solve.…”

Section: Introductionmentioning

confidence: 93%

“…A major problem for this approach is that it is not always possible to extract features on GOES images due to the presence of clouds, which makes it less reliable for a systematic evaluation of co-registration errors. A recent study by Grotenhuis et al 2 uses an area-based approach that involves calculating the cross-correlation of Earth-viewing pixels in different IR channels. In this approach, one of the two images from different IR channels is resampled using linear interpolation, and the correlation in brightness temperature between the resampled image and the image from the other channel is computed.…”

Section: Introductionmentioning

confidence: 99%

“…The GII4C algorithm uses the same approach as Grotenhuis et al 2 an area based approach to calculate correlations between images in different channels for Earth-viewing pixels. The key difference between this algorithm and the one in Ref.…”

Section: Introductionmentioning

confidence: 99%

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Geostationary Operational Environmental Satellite Imager infrared channel-to-channel co-registration characterization algorithm and its implementation in the ground system

Grotenhuis

et al. 2014

J. Appl. Remote Sens

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Channel-to-channel co-registration is an important performance metric for the Geostationary Operational Environmental Satellite (GOES) Imager, and large co-registration errors can have a significant impact on the reliability of derived products that rely on combinations of multiple infrared (IR) channels. Affected products include the cloud mask, fog and fire detection. This is especially the case for GOES-13, in which the co-registration error between channels 2 (3.9 μm) and 4 (10.7 μm) can be as large as 1 pixel (or ∼4 km) in the east-west direction. The GOES Imager IR channel-to-channel co-registration characterization (GII4C) algorithm is presented, which allows a systematic calculation of the co-registration error between GOES IR channel image pairs. The procedure for determining the co-registration error as a function of time is presented. The algorithm characterizes the co-registration error between corresponding images from two channels by spatially transforming one image using the fast Fourier transformation resampling algorithm and determining the distance of the transformation that yields the maximum correlation in brightness temperature. The GII4C algorithm is an area-based approach which does not depend on a fixed set of control points that may be impacted by the presence of clouds. In fact, clouds are a feature that enhances the correlations. The results presented show very large correlations over the majority of Earth-viewing pixels, with stable algorithm results. Verification of the algorithm output is discussed, and a global spatial-spectral gradient asymmetry parameter is defined. The results show that the spatial-spectral gradient asymmetry is strongly correlated to the co-registration error and can be an effective global metric for the quality of the channel-to-channel co-registration characterization algorithm. Implementation of the algorithm in the GOES ground system is presented. This includes an offline component to determine the time dependence of the co-registration errors and a real-time component to correct the co-registration errors based on the inputs from the offline component. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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Geostationary Operational Environmental Satellite Imager infrared channel-to-channel co-registration characterization algorithm and its implementation in the ground system

Grotenhuis

et al. 2014

J. Appl. Remote Sens

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“…However, even under normal operation of onboard systems, minor inaccuracies in INR may lead to temporal misalignments in TIR bands, which may reach half a pixel or more (Boeing, 2006), in addition to regularly occurring band-to-band misregistration of a smaller magnitude (Grotenhuis et al 2012). In addition, there have been several instances of a major failure of the INR components when image misalignment reached multi-pixel scales, including GOES-10 in 2006 and most recently, GOES-15 star tracker failure in April 2015.…”

Section: Multitemporal Anomaly Detection With Dynamic Detection Modelmentioning

confidence: 99%

The development and first validation of the GOES Early Fire Detection (GOES-EFD) algorithm

Koltunov

Ustin

Quayle³

et al. 2016

Remote Sensing of Environment

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Decades of successful active fire mapping from space, have led to global informational products of growing importance to scientific community and operational agencies. In contrast, detecting fires from space faster than current conventional capabilities in the continental U.S. has not been considered attainable, except in remote, sparsely populated areas. We present a research prototype version of the GOES Early Fire Detection (GOES-EFD) algorithm focused on minimizing the time to first detection of a wildfire incident. The algorithm is designed for regional-scale surveillance and combines multitemporal anomaly tests developed in our previous work, contextual hot-spot tests, and dynamic event classification and tracking. The GOES-EFD version 0.4 was initially tested with 40-day summer 2006 data over central California. The algorithm identified most of large (final size >2 ha) wildfires within 30 min and 31% of the wildfires were detected before they were reported by the public. Under identical operation conditions, GOES-EFD 0.4 provided quicker initial detection than the temporally filtered operational WF-ABBA algorithm (version 6.1) and committed fewer false alarms. There is a substantial potential for further reducing detection latency and increasing reliability. Following the ongoing optimizations, tests, and integration in collaboration with the fire Koltunov et al. GOES Early Fire Detection system 3 management agencies and first responders, GOES-EFD could be deployed for regional scale real-time surveillance to complement existing fire identification methods.

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“…The samples of these products are the cloud mask, fog, and the hot spot detection. Studies 9 have found that the Fig. 9 The same local area with the hot spot.…”

Section: Resampling On Goes Imagesmentioning

confidence: 99%

Fast Fourier transformation resampling algorithm and its application in satellite image processing

2014

J. Appl. Remote Sens

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Abstract. The image resampling algorithm, fast Fourier transformation resampling (FFTR), is introduced. The FFTR uses a global function in the Fourier expansion form to represent an image, and the image resampling is achieved by the introduction of a phase shift in the Fourier expansion. The comparison with the cubic spline interpolation approach in the image resampling is presented, which shows that FFTR is more accurate in the satellite image resampling. The FFTR algorithm is also generally reversible, because both the resampled and its original images share the same Fourier spectrum. The resampling for the images with hot spots is discussed. The hot spots in an image are the pixels with the second-order derivatives that are order of magnitude larger than the average value. The images with the hot spots are resampled with the introduction of a local Gaussian function to model the hot spot data, so that the remaining data for the Fourier expansion are continuous. Its application to the infrared channel image of Geostationary Operational Environmental Satellite Imager, to mitigate a diurnally changing band co-registration, is presented.

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On-orbit characterization of the GOES Imager channel-to-channel co-registration

Cited by 3 publications

References 3 publications

Geostationary Operational Environmental Satellite Imager infrared channel-to-channel co-registration characterization algorithm and its implementation in the ground system

Geostationary Operational Environmental Satellite Imager infrared channel-to-channel co-registration characterization algorithm and its implementation in the ground system

The development and first validation of the GOES Early Fire Detection (GOES-EFD) algorithm

Fast Fourier transformation resampling algorithm and its application in satellite image processing

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