Automated Georegistration of High-Resolution Satellite Imagery using a RPC Model with Airborne Lidar Information

Oh, Jaehong; Lee, Changno; Eo, Yang Dam; Bethel, James

doi:10.14358/pers.78.10.1045

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…Although the matching accuracy may be lower than that of feature-based matching, it has the advantage of performing image matching at a relatively low computational cost. Furthermore, it has been proven to be effectively applied to satellite images [7]. In this manuscript, image matching between GCP chips and satellite images was performed using normalized cross-correlation (NCC) and relative edge crosscorrelation (RECC), which are representative and conventional area-based matching techniques, and RPC bias compensation was performed using the matching results.…”

Section: Image Matching For Rpc Bias Compensationmentioning

confidence: 99%

“…However, the DNs of the GCP chip and satellite image may differ due to the acquisition time, seasonal differences, and radiometric differences, although the DNs of all image patches were normalized. Therefore, RECC can be used to overcome seasonal differences by exploiting edge information [7]. The RECC matching robust to radiometric and illumination characteristics with the low computational cost was applied using Eq.…”

Section: Image Matching For Rpc Bias Compensationmentioning

confidence: 99%

“…Traditionally, the georegistration of satellite data with high spatial resolution has been carried out manually. However, many automated approaches have been proposed based on the utilization of geospatial databases as reference data, such as light detection and ranging (LiDAR) [7], topographic maps [8], orthoimages [9], and DEM [10], [11]. LiDAR has high vertical accuracy, but the data size is large, and the available data is limited to urban areas.…”

Section: Introductionmentioning

confidence: 99%

“…GCP chip projection was carried out using initial RPCs with elevation information from 5-and 10-m digital surface models (DSMs) from national topographic maps. For image matching, normalized crosscorrelation (NCC) and relative edge cross-correlation (RECC) were tested to utilize the brightness and edge information [7], [23]. This manuscript is structured as follows.…”

Section: Introductionmentioning

confidence: 99%

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Automated RPCs Bias Compensation for KOMPSAT Imagery Using Orthoimage GCP Chips in Korea

Seo

Lee

et al. 2022

IEEE Access

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High-resolution satellite images, including KOMPSAT, WorldView-3, and Pléiades, are widely used for mapping and environmental monitoring. In particular, satellite images with high spatial resolution should have accurate location information to provide essential spatial information. To this end, most high-resolution satellite images files are provided with rational polynomial coefficients (RPCs) that can be used for the sensor modeling. However, the RPCs have an initial bias. Therefore, most satellite image platforms and researchers match satellite images using ground control points (GCPs) information and perform RPCs bias compensation of images using matching information. In Korea, an image-based GCP chip built using orthographic images is provided for the georegistration of various satellite images. In this manuscript, RPCs bias compensation of KOMPSAT-3A satellite images was performed using GCP chips, and then, the possibility of automation of RPCs bias compensation through GCP chip was analyzed. Image matching such as area-based and edge-based techniques were used, and the results of RPCs bias compensation using GCP chips were analyzed through experiments in various regions. The automated compensation in either areabased or edge-based matching performed well with within 1.5 pixel level of accuracy for test areas with various topographic features. However the forest area was challenging such that new GCP chips with rich feature information were required. In addition, edge-based matching could overcome large seasonal differences including snow cover.

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Section: Image Matching For Rpc Bias Compensationmentioning

confidence: 99%

Section: Image Matching For Rpc Bias Compensationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated RPCs Bias Compensation for KOMPSAT Imagery Using Orthoimage GCP Chips in Korea

Seo

Lee

et al. 2022

IEEE Access

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“…Furthermore, linear features can be an option to perform georegistration, instead of using point-based features. For example, Oh et al [ 20 ] proposed a relative edge cross-correlation (RECC) to extract corresponding linear features between VHR satellite images and airborne lidar data, in order to obtain 3D coordinates of the features exploited for the bias-compensation of RPCs.…”

Section: Introductionmentioning

confidence: 99%

Automated Geo/Co-Registration of Multi-Temporal Very-High-Resolution Imagery

Han

2018

Sensors

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For time-series analysis using very-high-resolution (VHR) multi-temporal satellite images, both accurate georegistration to the map coordinates and subpixel-level co-registration among the images should be conducted. However, applying well-known matching methods, such as scale-invariant feature transform and speeded up robust features for VHR multi-temporal images, has limitations. First, they cannot be used for matching an optical image to heterogeneous non-optical data for georegistration. Second, they produce a local misalignment induced by differences in acquisition conditions, such as acquisition platform stability, the sensor’s off-nadir angle, and relief displacement of the considered scene. Therefore, this study addresses the problem by proposing an automated geo/co-registration framework for full-scene multi-temporal images acquired from a VHR optical satellite sensor. The proposed method comprises two primary steps: (1) a global georegistration process, followed by (2) a fine co-registration process. During the first step, two-dimensional multi-temporal satellite images are matched to three-dimensional topographic maps to assign the map coordinates. During the second step, a local analysis of registration noise pixels extracted between the multi-temporal images that have been mapped to the map coordinates is conducted to extract a large number of well-distributed corresponding points (CPs). The CPs are finally used to construct a non-rigid transformation function that enables minimization of the local misalignment existing among the images. Experiments conducted on five Kompsat-3 full scenes confirmed the effectiveness of the proposed framework, showing that the georegistration performance resulted in an approximately pixel-level accuracy for most of the scenes, and the co-registration performance further improved the results among all combinations of the georegistered Kompsat-3 image pairs by increasing the calculated cross-correlation values.

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Geolocation bias correction for CARTOSAT-1 stereo images through virtual ground control points generation over mountainous areas

Tatar

2024

Earth Sci Inform

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Automated Georegistration of High-Resolution Satellite Imagery using a RPC Model with Airborne Lidar Information

Cited by 11 publications

References 17 publications

Automated RPCs Bias Compensation for KOMPSAT Imagery Using Orthoimage GCP Chips in Korea

Automated RPCs Bias Compensation for KOMPSAT Imagery Using Orthoimage GCP Chips in Korea

Automated Geo/Co-Registration of Multi-Temporal Very-High-Resolution Imagery

Geolocation bias correction for CARTOSAT-1 stereo images through virtual ground control points generation over mountainous areas

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