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Korea Ocean Satellite Center, KIOST, Ansan P.O.Box 29, Seoul 425-600, Korea. 2 ... Telecommunication & Computer Engineering, Korea Aerospace University, ...
Ocean Sci. J. (2012) 47(3):235-246 http://dx.doi.org/10.1007/s12601-012-0025-3

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Article

Geometric Performance Evaluation of the Geostationary Ocean Color Imager Chan-Su Yang1* and Jung-Hwan Song2 1

Korea Ocean Satellite Center, KIOST, Ansan P.O.Box 29, Seoul 425-600, Korea Electronic Engineering and Avionics, Telecommunication & Computer Engineering, Korea Aerospace University, Goyang 412-791, Korea

2

Received 12 April 2012; Revised 24 July 2012; Accepted 20 August 2012 © KSO, KIOST and Springer 2012

Abstract − The Geostationary Ocean Color Imager (GOCI) instrument acquires eight channels of multispectral images, which consist of 16 slots positioned in four lines and columns. GOCI Level 1B data, therefore, consist of a mosaic of 16 images, geometrically corrected with the Image Navigation and Registration Software Module (INRSM) system based on automatic point landmark matching for each slot and band. A study of the geometric performance characteristics of the Level 1B data was conducted over a period from August 2010 to September 2011 using residual data from Bands 7 and 8. To evaluate the geometric performance in detail, this paper examines the following four types of image navigation and registration errors: navigation performance, within-frame registration, frame-to-frame registration, and band-toband registration. In addition to the performance statistics based on mosaic images, we used a slot-based analysis method for the rainy season (here, June 2011) to understand the local distribution of the geometric performance. From the image-based results, the navigation and frame-to-frame accuracies were better than 1 pixel and the band-to-band registration accuracy was better than 0.4 pixels, while the within-frame registration accuracy was less than 1 pixel. However, for the band-to-band performance, the percentage of observations that fell within the specifications was slightly less than 99.7% for all 240 frames from June 2011. The within-frame performance was much lower than the other performance categories and the residual error for the east–west direction was higher than that for the north-south direction. The results from the slotbased performance evaluation suggest that abnormal errors (e.g. above 53 µrad for navigation) occur in some slots, although the performance during an estimation period of 7 continuous days was within the desired criteria. Key words − GOCI, geometric correction, geometric performance, residual analysis, navigation, registration *Corresponding author. E-mail: [email protected]

1. Introduction The Geostationary Ocean Color Imager (GOCI) instrument is one of three payloads onboard the Communication, Ocean, and Meteorological Satellite (COMS) that was launched into a geostationary orbit at 36,000 km from the Kourou Space Center, French Guiana, on June 27, 2010. Its sensor acquires eight channels of multispectral images that provide hourly coverage of a 2500 km2 area at 500 m resolution, with 16 slots positioned in four lines and columns, as shown in Fig. 1. The instrument requires 23 minutes to finish an observation from Band 1 to Band 8 for all slots. Since each slot is composed of eight spectral channels, and all the pixels within one slot and one channel are acquired at the same time using a matrix complementary metal-oxide-semiconductor (CMOS) detector array, a well-designed image navigation and registration (INR) system is required to compensate for geometric distortion when the 16-slot images are combined in a single image (Yang et al. 2007, 2010). The GOCI Image Navigation and Registration Software Module (INRSM) system was designed, developed, and tested by Astrium, the same company that built the GOCI instrument. The INRSM system processes the star, landmark, and orbit-ranging data in conjunction with the spacecraft attitude and orbit control electronics (AOCE) to maintain the pointing of the pixels (COMS INR Team 2007). The GOCI Level 1B image is a mosaic of 16 images from Level 1A, as displayed in Fig. 1, processed using the INRSM system. During the processing, landmark residuals are generated via automatic point landmark matching for each

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Fig. 1. GOCI observation area drawn for the GOCI data at 03:16 (UTC) on April 25, 2011. The inner 16 boxes show each tile of the mosaic data and the outer box gives the coverage of Level 1B generated from Level 1A Table 1. Image navigation and registration pre-launch specifications (3σ) for daytime GOCI Level 1B data Classes of criteria Requirements (µrad) Navigation accuracy 28 µrad Within-frame registration accuracy 28 µrad Registration accuracy between repeated 28 µrad images Band-to-band registration accuracy 7 µrad

slot and band and can be used to determine the image navigation and registration accuracy (COMS INR Team 2011). Table 1 shows the image navigation and registration prelaunch requirements based on the standard deviation (σ) of a Gaussian distribution of the residual differences calculated from satellite image navigation and registration data during the daytime. Geometrical image quality requirements for GOCI are 28 µrad (about 1 km) at nadir pointing for geolocation and registration within a frame or between images and 7 µrad for band-to-band co-registration. Different from the GOCI frame-capture method, a scanning method is generally used for meteorological geostationary satellites, such as the Multi-purpose Transmission Satellite (MTSAT) and Geostationary Operational Environmental Satellite (GOES). Since these satellites were designed for meteorological applications, the onboard sensors have very

low geometric, radiometric, and spectral resolutions (Gibbs, 2008). For example, MTSAT-2 provides a spatial resolution of 1km with a low signal-to-noise ratio (SNR) (6.5 @ 2.5% albedo) for the visible band (550 to 800 nm), compared to GOCI showing 0.5 km and 750 to 1200 for all bands, respectively (Kang et al. 2010). Therefore, there are few papers on geometric corrections for geostationary satellites compared to polar-orbit satellites (Yasukawa and Takagi 2003, Storey et al. 2004, Shimada et al. 2009). In this study, a landmark residual analysis is conducted to evaluate the geometric performance of the GOCI Level 1B (INRSM level) data over a period from August 2010 to September 2011 for navigation, and for within-frame, frame-toframe, and band-to-band registrations. By employing a slotby-slot analysis for the month of June 2011 during the rainy season, we investigate the distribution of landmark residuals at each slot and the effect of cloudy conditions on the performance statistics. The data and the geometric validation methodology are described in Section 2, and the performance estimation results for full images are analyzed in Section 3, including a correlation analysis between the performance and the number of landmarks. The performance properties at each slot are considered in Section 4, and we conclude the paper in Section 5.

2. Data and Method INRSM GOCI Level 1B is produced from Level 1A at the image pre-processing system (IMPS) level, which generates Level 0, Level 1A/1B, and image motion compensation (IMC) data, as well as radiometric/geometric correction coefficients from GOCI raw data. The IMPS consists of a decomposition module (DM), INRSM, IMPS database (DB), and product management module (PMM). The INRSM provides geometric corrections to the Level 1A radiometrically corrected input image files, and the Level 1B output image files, which include a header file and a pixel file. Fig. 2 shows a simplified block diagram including functions related only to the geometric corrections of the INRSM. The overall navigation error arising from all the contributors is a function of time, and the correction angles (CorrAngle in Fig. 2) are an estimation of the navigation error. When landmarks are available, the correction angles are estimated by the least-squares fit method (COMS INR Team 2007). When no landmark is available, a temporal

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(UTC) on April 25, 2011. The image in a pixel coordinate system can be converted to latitude–longitude coordinates represented as a grid in Fig. 3. Each output of GOCI level 1B consists of two files: a header file and pixel (mosaic image) data. The header file contains the image acquisition information, output data file descriptions, geo-referencing parameters used to locate each image pixel in earth coordinates, and characteristics of the INRSM processing performed for the image. The pixel file is in binary format and contains eight layers; each pixel value is coded in a 32-bit unsigned integer.

Fig. 2. Simplified INRSM block diagram

Fig. 3. Overlap of a 2° grid in latitude and longitude on the GOCI Level 1B image with pixel locations, generated for the same date shown in Fig. 1. The image is a composite of Bands 6, 4, and 2

correction of the slot is performed by linear regression from other slots containing visible landmarks to provide a linear error correction. When Level 1A image pixels are remapped to get Level 1B image pixels with an orthographic projection, rational polynomial coefficients (RPC) are estimated through a linear least-squares fit in the GOCI image resampling (COMS INR Team 2007, Seo et al. 2010). More details can be found in the related COMS INR Team documents. Fig. 3 shows a Level 1B image observed at 03:16–03:45

Landmarks The number and accuracy of landmarks are of prime importance for the overall INR performance (Lee et al. 2005). The landmark database contains 1180 landmarks. Their typical geographical distribution over the 16 slots and the number of landmarks per slot is shown in Fig. 4. The landmark database was generated from the Global Selfconsistent Hierarchical High-resolution Shoreline (GSHHS) shoreline database, but to improve matching performance, initial landmark chips were mostly replaced by real image chips (64×64 pixels) selected from the clear-sky parts of the GOCI images during in-orbit tests, as these provide better shoreline map matching using a cross-correlation method. GOCI landmarks are located along the shoreline (including lakes and rivers) and detected mostly depending on the cloud coverage at the acquisition time. Some landmarks can show degraded characteristics depending on the season, illumination conditions during the day, topography, and tide effects over certain coastal areas. Landmark measurements are obtained by correlating maps of prominent land–water interfaces with an edgedetecting transform of the received image pixel data. The shift in latitude and longitude required to maximize the correlation between the shoreline map and the transformed image data is added to the nominal landmark location to generate an observed landmark position. Separate landmark observations are obtained for Bands 7 and 8. Since Bands 7 and 8 are used for landmark matching with the landmark chips, residuals are only calculated for these bands. In general, there are more landmarks used for Band 8 than for Band 7. Slot 14 does not have any landmark chips, as shown in the right of Fig. 4. The temporal evolution of the number of landmarks follows the evolution of the cloud coverage, and this number sometimes drops to zero for several slots.

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Fig. 4. (a) Spatial distribution of landmarks used for the GOCI Level 1B image on the same date shown in Fig. 1 and (b) landmark numbers divided into slots

Geometric performance estimation method For GOCI image navigation and registration evaluation, the residual data of landmarks were extracted from 2,700 Level 1B images obtained for 13 months from August 2010 to September 2011. Since all numbers specified in the requirements of the INR system are based on the 3σ values for a Gaussian distribution, the performance is expressed in microradians of the absolute average plus three times the standard deviation of the residuals during a specific period called the “performance estimation period.” In this work, two periods were considered: 7 continuous days and 1 month. All landmarks residuals from INR processing of all image acquisitions during the chosen time period were gathered and the statistics were computed. The following four types of image navigation and registration errors were used in the performance estimation: navigation performance, within-frame registration, frame-to-frame registration, and band-to-band registration. The image navigation locates pixels relative to a fixed reference, and the image registration maintains the spatial relationship between pixels within images and between image frames. The navigation performance is estimated for all landmarks residuals acquired from Bands 7 and 8 during the performance estimation period for east–west (EW) and north–south (NS) directions and is related to the absolute image quality or the absolute geometric performance, while the registration

performance for within-frame, frame-to-frame, and bandto-band concerns the relative geometric performance. The within-frame registration accuracy is estimated from the relative residuals of all the landmarks computed for each image, for each pair of landmarks, and for both the EW and NS directions. A frame means 8 bands obtained at the same observation time as shown in Fig. 3. Of all available bands, 7 and 8 are used here for the within-frame registration because of the detectable band of landmark. The frame-to-frame registration accuracy is estimated from all relative residuals in all pairs of images for each landmark that is correctly matched within both images for both the EW and NS directions. The band-to-band registration accuracy is estimated for all relative residual pairs of Bands 7 and 8 of a slot. If we consider each band of a slot as an independent image, we can apply the same approach for the band-to-band and the frame-to-frame registration accuracies. In this study, we evaluated the hourly weekly and monthly INR performances based on the residual analyses for the GOCI Level 1B (INRSM level) data collected from August 2010 to September 2011. Performance statistics using four 1-week periods of residual data were calculated in order to compare with the INRSM requirements. Since a GOCI Level 1B image is a mosaic image of 16 slots, a slot-based performance analysis is also applied to estimate the INRSM performance, like an image-based

Geometric Performance Evaluation of the Geostationary Ocean Color Imager

analysis. In addition, the month June 2011, which is in the rainy season in Korea, was selected to investigate the effects of cloud cover on the geometric performance.

3. Statistic Results of the GOCI Image Navigation and Registration Landmark residuals of a GOCI image Fig. 5 shows the residual errors of valid landmarks found by the INRSM in the GOCI image taken as the second observation, which was started at 01:16:46 am (UTC) on June 18, 2011. The error values are divided into four categories: navigation and three registrations corresponding to within-frame, frame-to-frame, and band-to-band for both the EW and NS directions. As shown in Fig. 1, a slot is a small piece of a layer which includes 8 bands. A set of 16 slots is composed of eight-channel layers, corresponding to a frame obtained in the same observation period (approximately 30 minutes). In the other words, a frame has

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8-band images and each band image consists of 16 slots. In total, 364 valid landmarks were detected, which indicates that the image was largely affected by clouds. In the landmark determination, the system was set to use only Bands 7 and 8 since they provide excellent contrast between water and land, whereas the other bands do not. Totals of 168 of the landmarks were obtained from Band 7 and 196 from Band 8. Therefore, this is an example of a cloudaffected image. In general, the cloud detection results showed that Band 8 had more landmarks than Band 7 because the cloud ratio (%) of Band 8 was less than that of Band 7. The residual errors of navigation were between -16.77 and 21.92 µrad for the EW direction and between -27.38 and 14.22 µrad for the NS direction. The within-frame registration errors were in the -25.93 to 24.58 µrad (EW) and -21.48 to 26.69 µrad (NS) ranges, and the frame-toframe registration errors were in the -12.11 to 13.48 µrad (EW) and -20.78 to 7.83 µrad (NS) ranges. From the residual

Fig. 5. Distributions of landmark residuals for (a) navigation, (b) within-frame registration, (c) frame-to-frame registration, and (d) band-to-band registration using the single image obtained at the second observation (01:16 a.m.) on June 18, 2011. At nadir, 500 m on the Earth’s surface corresponds to an optical angle of about 14 µrad

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distribution of Fig. 5, the within-frame registration errors were more widespread, and the band-to-band residuals mostly converged to the center value of standard deviations. The landmark band-to-band residuals in the NS direction (-15.88 to 4.50 µrad) were not within the requirements, although the EW errors ranged from -4.49 to 6.08 µrad. The geometric performance can be assessed by all landmark residuals in a single image and a pair of continuous images or frames. According to the results, the performance was degraded with respect to the cloudy conditions (for example, within-frame EW: 26.18 µrad), but remained within the specifications for both the EW and NS directions. GOCI INR performance: Monthly variations The GOCI INR system-level performance was evaluated for all INR categories of navigation and three registrations (within-frame, frame-to-frame, and band-to-band) based on the monthly residual statistics calculated for the images from August 2010 to September 2011. The GOCI INR assessments were performed with a reference catalog of 1180 landmarks. The GOCI landmarks were detected according to the cloud coverage at the acquisition time. The resulting landmark detection rate and navigation error are shown in Fig. 6, expressed as a monthly average and its standard deviation based on the number of landmarks and performance for hourly whole images. The ratio of zero landmarks was approximately 2.4%. The monthly minimum number of landmarks obtained during the period was 499 (June 2011), which is above the

Fig. 6. Monthly fluctuations of navigation performance (3σ value) and number of landmarks (LMKS) from August 2010 to September 2011. NAV_EW and NAV_NS indicate navigation performance for the EW and NS directions, respectively

minimum obtained for the hourly image analyses, e.g., 364 at 01:16 am on June 18, 2011. A distinctive feature of the number of landmarks is that it decreased in summer and increased in the other seasons. In Korea, summer starts in June and ends in August and is considered the rainy season. Some typhoons are also present at this time in the northwest Pacific region. Cloudy conditions explain why very few landmarks appear in the summer period from June to August 2011. The landmarks shown in Fig. 6 are consistent in terms of the monthly trends with seasonal weather variation, especially cloud cover. The navigation statistics showed a slight tendency of the navigation errors to increase over the whole period, and there were high negative correlations of 0.59 (EW) and 0.67 (NS) between the navigation performance and the number of landmarks. The monthly navigation errors were mainly affected by the number of landmarks, especially during the period from April to September 2011. The average number of landmarks was 776 for 2651 images, and the performance statistics of the navigation accuracy were 13.2 and 10.4 µrad for the EW and NS directions, respectively. The performance error was higher in the EW direction than in the NS direction. In the monthly averages of navigation errors, the navigation performance was well within the specifications and showed a correlation with the number of landmarks. Fig. 7 shows a time series of the accuracies for the withinframe, frame-to-frame, and band-to-band registrations during the period from August 2010 to September 2011. These registration accuracies were calculated for relative residual values. The statistics were 18.5 and 14.7 µrad for the within-frame registration, 12.3 and 9.8 µrad for the frameto-frame registration, and 5.1 and 4.7 µrad for the band-toband registration for the EW and NS directions, respectively. The performance results were all within the desired criteria. The within-frame registration errors were high compared to the other performance categories, as shown in the singleimage analysis. The performance errors for the EW direction were higher than those for the NS direction. The reason could be that EW orbit determination is less accurate than NS direction. For example, the proposed requirement for ranging accuracy from the ground station is 18 km in EW direction and 6km NS direction (COMS INR Team, 2007). The within-frame registration errors showed an increasing trend over the period, while the band-to-band registration accuracy slightly decreased after showing a peak in January

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accuracy is a function of ranging, thermal elasticity, attitude and orbit control system and payload contribution. Therefore, it can be said that the first refinement of state vector and the detected landmarks will impact the temporal variation of geometric errors. GOCI INR performance: Hourly variations June 2011 was selected to examine the hourly variations in the GOCI INR performance because the navigation, within-frame, and landmark performances were the lowest in June 2011. Fig. 8 shows a time series of the navigation

Fig. 7. Same as Fig. 6, except showing the (a) within-frame registration accuracy, (b) registration accuracy between repeated images, and (c) band-to-band registration accuracy. The scales of the relative residuals in each plot differ to show the detailed variations

2011. Therefore, the correlation coefficient between the landmark and band-to-band error was very low, about 0.27. The association was strongest for the frame-to-frame registration errors and number of landmarks, above -0.73. The INR processing is based on an iterative refinement of a state vector that is supposed to model the geometry of the system: orbit, attitude, orientation between sensors and instruments, instrument geometry. Landmark data are then detected for a most accurate refinement. Finally, since those landmark data give absolute reference with respect to the Earth, for the whole INR performance the number and the accuracy of the landmarks are of prime importance. The spatial distribution of landmarks changes between acquisitions according to the weather and the temporal evolution of the number of landmarks follows the evolution of the cloud coverage. Before INR processing the navigation

Fig. 8. Geometric performance calculated from landmark residuals for June 2011. All performance values are 3σ (99.7 percentile) for navigation (NAV), within-frame registration (WIFR), frame-to-frame registration (FFR), and band-to-band registration (BBR), and are divided into both EW and NS directions

Fig. 9. Weekly performance statistics (3σ value) of the GOCI INR for June 2011. The ‘EW’ and ‘NS’ at the end of each legend item indicate the direction. The other abbreviations are the same as in Fig. 8

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error, along with the within-frame, frame-to-frame, and band-to-band registration errors based on a scene-by-scene analysis for June 2011. The corresponding weekly performance statistics are shown in Fig. 9. The performance was based on the 3σ value from each frame. There were two images out of a total of 240 where landmarks were not available during the period. The data without landmarks were excluded from the analysis of normal operations. In Fig. 8, the performance fluctuations were large, especially for the frame-to-frame and within-frame registrations in the EW direction, with standard deviations of 2.7 and 2.5 µrad, respectively. Even though a cyclical change

appeared over 4 or 5 days in the plot, the interrelationships between these two parameters were low. As shown in Fig. 9, on average, the EW errors were much higher than the NS errors, and the maximum errors occurred in the withinframe registration. The difference between the two directions in the band-to-band registration was the smallest, which is similar to the trend of the monthly averaged data shown in Fig. 7. In the month of June 2011, the number of landmarks was not particularly associated with the performance of the daily and weekly data. The INR system level performance can be measured as the percentage of observations that fall within specifications in a

Table 2. Percentage (%) of landmarks in the GOCI INR requirements for the month of June 2011. The statistics are given based on all 240 images Navigation Within-Frame Frame-to-Frame Band-to-Band June 2011 EW NS EW NS EW NS EW NS 1st week 99.91 99.98 99.80 99.95 99.95 99.94 99.24 98.34 2nd week 99.95 99.96 99.88 99.92 99.97 99.92 99.45 98.80 3rd week 99.94 99.98 99.81 99.93 99.98 99.97 99.74 99.37 4th week 99.97 99.96 99.88 99.90 99.96 99.94 99.45 98.41

Fig. 10. Geometric performance for each slot of the single-image data obtained at the second observation (01:16 a.m.) on June 18, 2011. All performance values are 3σ (99.7 percentile) for (a) navigation, (b) within-frame, (c) frame-to-frame, and (d) band-to-band for both the EW and NS directions. Blank bars indicate slots that have no landmarks. The average value per slot is shown in this figure

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given category. These requirements were also calculated at the 3σ level. The interpretation of these requirements is that at least 99.7% of the measurement population should remain within the specifications. Table 2 lists the percentage of frame rates within the requirements out of 240 frames. Over 98.34% of available landmarks met all of the observational requirements for normal operations. For the navigation and within-frame data, the EW percentages were lower than the NS percentages, but the reverse was found for the frame-toframe and band-to-band data. The band-to-band percentage for the NS direction was the lowest of all, 98.34% for the first week. Therefore, the band-to-band performance was slightly outside the requirements.

a.m.) on June 18, 2011, are shown for the 16 slots in Fig. 9. The performance results were within navigation and frameto-frame registration error budget. Two slots exceeded 28 µrad for the within-frame registration, and Slot 8 was 12 µrad for the band-to-band registration. Slots 7 and 8 showed comparatively high errors for navigation and within-frame registration, while the band-to-band errors of Slots 5 and 8 were only large in the NS direction. Therefore, the slot-based results did not perform well for some of the slots, although the residual errors were all within the criteria for the image-based performance assessment. In addition, many slots had no landmarks detected from the INRSM and are expressed as blank bars in the figure.

4. Effects of Slots on the Geometric Performance

Temporal change of the slot-based geometric performance Figs. 11 and 12 show the weekly performance statistics for the navigation, as well as the within-frame, frame-toframe, and band-to-band registration errors based on a slotby-slot analysis over June 2011 for the EW and NS directions,

Slot-based geometric performance for a single image The average INR performance statistics of each slot for the single image obtained at the second observation (01:16

Fig. 11. Weekly performance statistics (3σ value) of the GOCI INR for June 2011, corresponding to the EW direction for each slot. The weekly average of geometric errors per slot is shown in this figure

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Fig. 12. Same as Fig. 11, except for the NS direction

respectively. The performance results were all within the criteria except for Slot 1 during the second week for the within-frame EW direction. Large temporal performance changes were evident for some slots (e.g. Slot 1 in Fig. 11 and Slot 9 in Fig. 12), but overall there was a tendency for the values to remain in a small range over the analysis period. For example, for navigation in the EW direction, Slots 1, 4, and 9 belonged to a group that represented low performance, while Slots 2, 6, and 13 showed high performance throughout the period. In the EW direction, the maximum difference for navigation was 12 µrad between Slots 1 and 2 during the second week, while for withinframe registration it was 16 µrad between Slots 7 and 13 during the fourth week, for frame-to-frame registration it was 12 µrad between Slots 4 and 1 during the second week, and for band-to-band registration it was 6 µrad between Slots 12 and 1 during the first week. In general, the biggest performance difference between slots was during the second or fourth weeks, except for the band-to-band performance. The within-frame performance value was approximately

four times the band-to-band value. The NS performance was better than the EW performance, as shown in Fig. 12, and the difference between slots was also less in the NS direction. The band-to-band performance of Slot 9 had higher errors compared to other slots in the image navigation, within-frame registration, and frame-to-frame registration. Table 3 lists the 3σ error metrics of the worst performance values for each slot, calculated from the image navigation and registration processing during June 2011. For navigation, the maximum values were 70.70 µrad at Slot 5 and 53.54 µrad at Slot 3 for the EW and NS directions, respectively. This means that the navigation performance exceeded the criterion by a great deal. This result can be also seen in the registration statistics tables. For the within-frame registration, the performance errors were above 82 µrad and were almost three times the criterion. Fig. 13 presents the temporal variance in the slot number without landmarks in images taken during June 2011. The mean number was 6.6 slots, equivalent to 41.3% of 16 slots.

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Table 3. 3σ error metrics of worst performances for navigation, within-frame, frame-to-frame, and band-to-band during June 2011 Navigation Within Frame Frame-to-Frame Band-to-Band June 2011 EW NS EW NS EW NS EW NS Worst (µrad) 70.70 53.54 115.00 82.88 76.14 76.32 25.72 18.93 Slot Slot 5 Slot 3 Slot 5 Slot 3 Slot 5 Slot 6 Slot 12 Slot 9 Date June 21 June 21 June 21 June 6 June 21 June 14 June 6 June 17 Observation Time 05:16 05:16 05:16 07:16 06:16 06:16 06:16 02:16

Fig. 13. Temporal variance of the number of slots without landmarks in images taken during June 2011

That is, on average, 9.4 slots were used for the GOCI INR, and local areas require special care in handling for research. The number 16 means that no landmarks were available in any slot. This number sometimes dropped to zero for several of the 16 slots. The total number of observations with zero landmarks was two.

5. Concluding Remarks GOCI is the first payload that allows a ground resolution of 500 m in geostationary orbit. Its Level 1B data are resampled from 16-slot images of Level 1A data. The GOCI INR system level performance was evaluated using the residual errors of valid landmarks extracted from Bands 7 and 8 produced by the INRSM. Through the statistical analysis of geometric performance, we have attempted to demonstrate how geometric errors within an image frame and a slot over short- and long-term periods occurred for navigation and three registration items. In terms of monthly and weekly performance averages, GOCI Level 1B met all the observational requirements. However, for hourly data the percentage of observations

that fell within specifications ranged from 98.34 to 99.98%. The band-to-band percentage was mostly less than 99.7% during June 2011. The slot-based results indicated that there could be many slots that had no valid landmarks. Abnormal performance errors were defined as more than twice the requirements. The residual errors of the within-frame registration and the EW direction were much higher. For all INR requirements pertaining to normal operations, INR specification compliance greater than 98.34% was achieved, but for a regional study of one or two slots, the data required special care in handling. In terms of pixel-based accuracy, the navigation and frame-to-frame accuracies were better than 1 pixel, and the band-to-band registration accuracy was better than 0.4 pixels, while the within-frame registration accuracy was less than 1 pixel. Future studies will focus on defining geometric error sources that occur at each landmark. We can employ the results shown in this study to improve the performance of the image navigation and registration for the upcoming GOCI-II instrument.

Acknowledgements This work was supported in part by a grant from the projects “Support for research and applications of GOCI (PM56890)” and “Development of Korea Operational Oceanographic System (PM57041)” funded by the Ministry of Land, Transport and Maritime Affairs of Korean government and the Basic Research Projects (PE98781, and PE98732) of the Korea Institute Ocean Science and Technology.

References COMS INR Team (2007) Image quality and INR system error analysis report (SYS 20). COMS Technical Paper, COMS.TN.00109. DP.T.ASTR, 95 p COMS INR Team (2011) COMS INR In-orbit test report including image quality verification report (SYS-13). COMS Technical

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Paper, COMS.RP.00347.DP.T.ASTR, 59 p Gibbs B (2008) Feature: GOES image navigation and registration. Satmagazine (July 2008). http://www.satmagazine.com/cgibin/display_article.cgi?number=1889485593 Kang GS, Coste P, Youn HS, Faure F, Choi SB (2010) An In-Orbit Radiometric Calibration Method of the Geostationary Ocean Color Imager. IEEE Trans Geosci Remote Sens 48(12):43224328 Lee TY, Kim TJ, Choi HJ (2005) Automated landmark extraction based on matching and robust estimation with geostationary weather satellite images. Korean J Remote Sens 21(6):505516 Seo SB, Lim HS, Ahn SI (2010) Introduction to image proprocessing subsystem of Geostationary Ocean Color Imager (GOCI). Korean J Remote Sens 26(2):167-173 Shimada M, Isoguchi S, Tadono T, Isono K (2009) PALSAR radiometric and geometric calibration. IEEE Trans Geosci

Remote Sens 47(3): 3915-3932 Storey JC, Choate MJ, Meyer DJ (2004) A geometric performance assessment of the EO-1 Advanced Land Imager. IEEE Trans Geosci Remote Sens 42(3):602-607 Yang CS, Cho SI, Han HJ, Yoon S, Kwak KY, Ahn YH (2007) Development of Korea Ocean Satellite Center (KOSC): System design on reception, processing and distribution of Geostationary Ocean Color Imager (GOCI) data. Korean J Remote Sens 23(2):137-144 Yang CS, Bae SS, Han HJ, Ahn YH, Ryu JH, Han TH, Yoo HR (2010) Introduction of acquisition system, processing system and distributing service for Geostationary Ocean Color Imager (GOCI) data. Korean J Remote Sens 26(2):263-275 Yasukawa M, Takagi M (2003) Geometric correction of GMS SVISSR using elevation distortion compensation. Japanese J Photogra Remote Sens 42(6):33-41 (in Japanese)