Automated Bias-Compensation Approach for Pushbroom Sensor Modeling Using Digital Elevation Model

Oh, Kwan-Young; Jung, Hyung-Sup

doi:10.1109/tgrs.2016.2517100

Cited by 13 publications

(7 citation statements)

References 28 publications

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“…The initial ground coordinates of the 100 TPs, which are estimated using initial RPCs, have systematic errors. It may be assumed that these errors are large in absolute terms, but relatively small; that is, while Based on this assumption, the initial ground coordinates of the TPs can be refined by matching the heights of the initial ground coordinates with those of the DEM [8,9]. Through this process, the TPs are calibrated, in terms of their ground position, and then used as virtual GCPs.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, the stereo geometry is optimal. used for automated bias-compensation method [8]. In 2014, NGII tested the accuracy of the height data of their SRTM DEM and NGII DEM for the entire area of South Korea (Table 3).…”

Section: Study Area and Kompsat-3 Stereo Imagesmentioning

confidence: 99%

“…Additionally, Lee et al [7] predicted that errors in KOMPSAT-3 sensor modeling will not exceed 3 m, even when only one GCP is used. Furthermore, Oh et al [8,9] introduced an automatic bias-compensation method for the KOMPSAT series; when using the SRTM DEM as reference data, it achieved a sensor modeling accuracy of 10 m or less in the X, Y, and Z directions. Studies aimed at establishing the stability of stereo geometry have also been carried out: Jeong [4] analyzed the compatibility of stereo geometry data with the 3D positioning data of a stereo image pair, as generated by the same and different types of satellites, and showed that when the stereo geometry is unstable, the accuracy of 3D positioning accuracy of the stereo image pair may be reduced.…”

Section: Introductionmentioning

confidence: 99%

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Quality Assessment of Four DEMs Generated Using In-Track KOMPSAT-3 Stereo Images

Lee

Jeong

et al. 2019

Journal of Sensors

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The purpose of this research was to analyze the quality and characteristics of four digital elevation models (DEMs) generated using in-track Korea Multi-Purpose Satellite (KOMPSAT)-3 stereo images. The sensor modeling methods were based on ground control points (GCPs), the initial rational polynomial coefficients (RPCs), relative adjustment, and the automatic bias-compensation method. The GCPs and check points (CPs) were extracted from the 0.25 m aerial orthoimage and the 5 m DEM provided by the National Geographic Information Institute (NGII). The DEMs had the same resolution as the reference DEM (5 m) and comparative analysis was carried out. The results indicate that when relative adjustment was applied alone (DEM 3), the percentage of matched points with a correlation of 0.8 or more was improved by at least 17% compared to the case where only initial RPCs were used (DEM 2). Although the absolute horizontal position error of DEM 3 could not be eliminated, the relative elevation error at the same position was reduced significantly. Therefore, if the relative positions of DEMs produced at different times can be corrected, they can be used for the detection of changes in altitude. When applying the automatic bias-compensation method (DEM 4) without GCPs, the percentage of matched points with a correlation of 0.8 or more was 70.1%. When GCPs were used (DEM 1), the value was 70.2%, i.e., almost identical to that of DEM 4. The mean difference in resolution among DEMs 1 and 4 was -1.8 ± 3.4 m (median, -1.0 m). The results show that DEMs of sufficient quality can be generated without GCPs. Furthermore, although discrepancies among the DEMs were noted in forest and shadow areas, it is possible to produce a 5~10 m resolution DEM by using additional image processing techniques, such as shadow removal.

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Section: Methodsmentioning

confidence: 99%

Section: Study Area and Kompsat-3 Stereo Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quality Assessment of Four DEMs Generated Using In-Track KOMPSAT-3 Stereo Images

Lee

Jeong

et al. 2019

Journal of Sensors

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“…The KOMPSAT-2 satellite was launched in July 2006 and is currently in operation [46,47]. It provides four channel images, including blue, green, red, and NIR wavelength bands and has a spatial resolution of 4 m. The KOMPSAT-2 image used for this test was acquired on 2 May 2010 in the Northern part of the Gulf of Mexico at a latitude and longitude of 28 • 45 N to 28 • 50 N and 88 • 19 W to 88 • 25 W, respectively (Figure 1).…”

Section: Study Area and Datasetmentioning

confidence: 99%

Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network

2020

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Oil spill accidents in marine environments have a massive impact on ecosystems. Various methods have been developed to detect oil spills using high-resolution optical imagery. However, ocean waves caused by heavy winds occurring in the accident area cause sun glint in the image, and this severely impedes the ability to detect the oil spill area. The objective of this study was to detect oil spill areas from high-resolution optic images using the artificial neural network (ANN) through effective suppression of severe sun glint effects. To enable this, a directional median filter (DMF) was adapted, and its use was compared with that of a traditional low-pass filter. A performance test was conducted using a KOMPSAT-2 image acquired during oil spill accidents that occurred in the Gulf of Mexico in 2010. The proposed method involved two main steps: (i) The sun glint effects caused by the ocean waves were corrected using the DMF; and (ii) the ANN approach was used to detect the oil spill area. The results show the following: (i) The designed DMF, which considers the size and angle of ocean waves, was proficient in correcting the sun glint effect in a high-resolution optical image; and (ii) oil spill areas were efficiently detected using the ANN approach with the proposed filtering method. The oil spill area was classified with accuracies of approximately 98.12% and 89.56% using the receiver operating characteristic (ROC) curve and probability of detection (POD) measurements, respectively. These results show that the accuracy of the proposed method is improved by about 9% compared to the traditional detecting algorithm.

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“…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%

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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Automated Bias-Compensation Approach for Pushbroom Sensor Modeling Using Digital Elevation Model

Cited by 13 publications

References 28 publications

Quality Assessment of Four DEMs Generated Using In-Track KOMPSAT-3 Stereo Images

Quality Assessment of Four DEMs Generated Using In-Track KOMPSAT-3 Stereo Images

Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network

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

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