• Korean Journal of Remote Sensing
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• v.18 no.2
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• pp.97-105
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• 2002

In this paper, we proposed a simplified strategy for the epipolarity of linear pushbroom imagery. The proposed strategy is verified on "Gupta and Hartly" sensor model and "Orun and Natarajan" sensor model. It is also compared with the precise epipolarity model of each sensor model on SPOT and KOMPSAT imagery. For the quantitative analysis, 20 ground control points are used as independent checking points. Based on the results, the accuracy of the proposed strategy is not different from that of the precise epipolarity model of each sensor model (below 0.1 pixels). Under the worst circumstance, the proposed strategy is robust. We can assure that the proposed strategy will show high accuracy on most of sensor models based on the co-linearity equations.

• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.9-15
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• 2011

Since PAN(panchromatic) and MS(multispectral) imagery of pushbroom scanner have the offset between PAN and MS CCD(charge coupled device) in the focal plane, PAN and MS images are acquired at different time and angle. Since clouds are fast moving objects, they should lead mis-registration problem with wrong matching points on clouds. The registration of cloudy imagery to recognize and remove the contamination of clouds can be categorized into three classes: (1) cloud is considered as nose and removed (2) employing multi-spectral imagery (3) using multi-temporal imagery. In this paper, method (1) and (3) are implemented and analysed with cloudy pushbroom scanner images.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.543-547
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• 2002

In this paper, we introduce a more improved camera modeling method for linear pushbroom images than the method proposed by Orun and Natarajan(ON). ON model shows an accuracy of within 1 pixel if more than 10 ground control points(GCPs) are provided. In general, there is high correlation between platform position and attitude parameters but ON model ignores attitude variation in order to overcome such correlation. We propose a new method that obtains an optimal solution set of parameters without ignoring the attitude variation. We first assume that attitude parameters are constant and estimate platform position's. Then we estimate platform attitude parameters using the values of estimated position parameters. As a result, we can set up an accurate camera model for a linear pushbroom satellite scene. In particular, we can apply the camera model to its surrounding scenes because our model provide sufficient information on satellite's position and attitude not only for a single scene but also for a whole imaging segment. We tested on two images: one with a pixel size 6.6m$\times$6.6m acquired from EOC(Electro Optical Camera), and the other with a pixel size 10m$\times$l0m acquired from SPOT. Our camera model procedures were applied to the images and gave satisfying results. We had obtained the root mean square errors of 0.5 pixel and 0.3 pixel with 25 GCPs and 23 GCPs, respectively.

• Proceedings of the KSRS Conference
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• v.2
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• pp.860-863
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• 2006

Epipolar images have to be generated to stereo display aerial images or satellite images. Pushbroom sensor is used to acquire high resolution satellite images. These satellite images have curvilinear epipolar lines unlike the epipolar lines of frame images, which are straight lines. The aforementioned fact makes it difficult to generate epipolar images for pushbroom satellite images. If we assume a linear transition of the sensor having constant speed and attitude during image acquisition, we can generate epipolar images based on parallel projection model (2D Affine model). Recent high resolution images are provided with RPC values so that we can exploit these values to generate epipolar images without using ground control points and tie point. This paper provides a procedure based on the parallel projection model for generating epipolar images directly from a stereo IKONOS images, and experimental results.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.451-456
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• 2006

Epipolar images have to be generated to stereo display aerial images or satellite images. Pushbroom sensor is used to acquire high resolution satellite images. These satellite images have curvilinear epipolar lines unlike the epipolar lines of frame images, which are straight lines. The aforementioned fact makes it difficult to generate epipolar images for pushbroom satellite images. If we assume a linear transition of the sensor having constant speed and attitude during image acquisition, we can generate epipolar images based on parallel projection model (20 Affine model). Recent high resolution images are provided with RPC values so that we can exploit these values to generate epipolar images without using ground control points and tie point. This paper provides a procedure based on the parallel projection model for generating epipolar images directly from a stereo IKONOS images, and experimental results.

• Korean Journal of Remote Sensing
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• v.18 no.4
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• pp.233-242
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• 2002

In this paper, an epipolar model without the ephemeris data is proposed. Also, various epipolar models such as the epipolar geometry of perspective sensor, the one proposed by Gupta and Hartley and the one based on the Orun and Natarajan's sensor model are reviewed and their accuracy are quantitatively analyzed using devised measure. Modeling data from ground control points, ground control points, ephemeris data and independent checking points are selected on SPOT over Taejon and Boryung area and KOMPSAT over Taejon and Nonsan area. Based on the results, the epipolar model of perspective sensor and the one by Gupta and Hartley have the average accuracy within 1 pixel but show high errors in several checking points. The proposed epipolarity model provides better results than that of perspective sensor and by Gupta and Hartley. Also, it shows the accuracy similar to the one based on Orun and Natarajan's sensor model.

• Korean Journal of Geomatics
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• v.2 no.2
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• pp.139-143
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• 2002

The rational polynomial coefficients (RPC) model is a generalized sensor model that is used as an alternative solution for the physical sensor model for IKONOS of the Space Imaging. As the number of sensors increases along with greater complexity, and the standard sensor model is needed, the applicability of the RPC model is increasing. The RPC model has the advantages in being able to substitute for all sensor models, such as the projective, the linear pushbroom and the SAR. This report aimed to generate a RPC model from the physical sensor model of the KOMPSAT-1(Korean Multi-Purpose Satellite) and aerial photography. The KOMPSAT-1 collects 510~730 nm panchromatic imagery with a ground sample distance (GSD) of 6.6 m and a swath width of 17 km by pushbroom scanning. The least square solution was used to estimate the RPC. In addition, data normalization and regularization were applied to improve the accuracy and minimize noise. This study found that the RPC model is suitable for both KOMPSAT-1 and aerial photography.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.4 s.22
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• pp.41-50
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• 2002

RPM is the one of the sensor models which is proposed by Open GIS Consortium (OGC) as image transfer standard. And it is the sensor model for end-users using IKONOS, a commercial pushbroom satellite, imagery which provide about 1m ground resolution. Parameters called RPC which is IKONOS RFM coefficients are serviced to end-users. But if some users try to make additional effort to get rigorous geo-spatial information, it is necessary to apply mathematic or abstract sensor models, because vendors don't offer any ancillary data for physical sensor models such as satellite orbit and navigation. Abstract sensor models such as pushbroom Direct Linear Transform (DLT) require many GCPs well distributed in imagery, and mathematic sensor model such as RFM, polynomials need much more GCPs. Therefore RPC modification using additional a few GCPs is the best solution. In this paper, two methods are proposed to modify RPC. One is method to use pseudo GCPs generated in normalized cubic, and another method uses parameters observations and a few GCPs. Through two methods, we get improvement of accuracy 50% and over.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.417-422
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• 2003

The KOMPSAT-2 MSC(Multi-Spectral Camera), with high spatial resolution, is currently under development and will be launched in the end of 2004. A sensor model relates a 3-D ground position to the corresponding 2-D image position and describes the imaging geometry that is necessary to reconstruct the physical imaging process. The Rational Function Model (RFM) has been considered as a generic sensor model. form. The RFM is technically applicable to all types of sensors such as frame, pushbroom, whiskbroom and SAR etc. With the increasing availability of the new generation imaging sensors, accurate and fast rectification of digital imagery using a generic sensor model becomes of great interest to the user community. This paper describes the procedure to generation of the RPC (Rational Polynomial Coefficients) for KOMPSAT-2 MSC.

• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.140-148
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• 2001

The satellite sensor model is typically established using ground control points acquired by ground survey Of existing topographic maps. In some cases where the targeted area can't be accessed and the topographic maps are not available, it is difficult to obtain ground control points so that geospatial information could not be obtained from satellite image. The paper presents several satellite sensor models and satellite image decomposition methods for non-accessible area where ground control points can hardly acquired in conventional ways. First, 10 different satellite sensor models, which were extended from collinearity condition equations, were developed and then the behavior of each sensor model was investigated. Secondly, satellite images were decomposed and also pseudo images were generated. The satellite sensor model extended from collinearity equations was represented by the six exterior orientation parameters in 1$^{st}$, 2$^{nd}$ and 3$^{rd}$ order function of satellite image row. Among them, the rotational angle parameters such as $\omega$(omega) and $\phi$(phi) correlated highly with positional parameters could be assigned to constant values. For non-accessible area, satellite images were decomposed, which means that two consecutive images were combined as one image. The combined image consists of one satellite image with ground control points and the other without ground control points. In addition, a pseudo image which is an imaginary image, was prepared from one satellite image with ground control points and the other without ground control points. In other words, the pseudo image is an arbitrary image bridging two consecutive images. For the experiments, SPOT satellite images exposed to the similar area in different pass were used. Conclusively, it was found that 10 different satellite sensor models and 5 different decomposed methods delivered different levels of accuracy. Among them, the satellite camera model with 1$^{st}$ order function of image row for positional orientation parameters and rotational angle parameter of kappa, and constant rotational angle parameter omega and phi provided the best 60m maximum error at check point with pseudo images arrangement.