• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.581-588
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• 2018

The aim of this study is to propose a fine co-registration approach for multi-temporal satellite images acquired from RapidEye, which has an advantage of availability for time-series analysis. To this end, we generate multitemporal ortho-rectified images using RPCs (Rational Polynomial Coefficients) provided with RapidEye images and then perform fine co-registration between the ortho-rectified images. A DEM (Digital Elevation Model) extracted from the digital map was used to generate the ortho-rectified images, and the RNCC (Registration Noise Cross Correlation) was applied to conduct the fine co-registration. Experiments were carried out using 4 RapidEye 1B images obtained from May 2015 to November 2016 over the Yeonggwang area. All 5 bands (blue, green, red, red edge, and near-infrared) that RapidEye provided were used to carry out the fine co-registration to show their possibility of being applicable for the co-registration. Experimental results showed that all the bands of RapidEye images could be co-registered with each other and the geometric alignment between images was qualitatively/quantitatively improved. Especially, it was confirmed that stable registration results were obtained by using the red and red edge bands, irrespective of the seasonal differences in the image acquisition.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.491-498
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• 2019

This study analyzed how the accuracy of co-registration varies depending on the convergence angles between two KOMPSAT-3·3A images. Most very-high-resolution satellite images provide initial coordinate information through metadata. Since the search area for performing image co-registration can be reduced by using the initial coordinate information, in this study, the mutual information method showing high matching reliability in the small search area is used. Initial coarse co-registration was performed by using multi-spectral images with relatively low resolution, and precise fine co-registration was conducted centering on the region of interest of the panchromatic image for more accurate co-registration performance. The experiment was conducted by 120 combination of 16 KOMPSAT-3·3A 1G images taken in Daejeon area. Experimental results show that a correlation coefficient between the convergence angles and fine co-registration errors was 0.59. In particular, we have shown the larger the convergence angle, the lower the accuracy of co-registration performance.

• Journal of Mechanical Science and Technology
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• v.19 no.12
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• pp.2213-2223
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• 2005

In order to reconstruct a full 3D human model in reverse engineering (RE), a 3D scanner needs to be placed arbitrarily around the target model to capture all part of the scanned surface. Then, acquired multiple scans must be registered and merged since each scanned data set taken from different position is just given in its own local co-ordinate system. The goal of the registration is to create a single model by aligning all individual scans. It usually consists of two sub-steps: rough and fine registration. The fine registration process can only be performed after an initial position is approximated through the rough registration. Hence an automated rough registration process is crucial to realize a completely automatic RE system. In this paper an automated rough registration method for aligning multiple scans of complex human face is presented. The proposed method automatically aligns the meshes of different scans with the information of features that are extracted from the estimated principal curvatures of triangular meshes of the human face. Then the roughly aligned scanned data sets are further precisely enhanced with a fine registration step with the recently popular Iterative Closest Point (ICP) algorithm. Some typical examples are presented and discussed to validate the proposed system.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.221-232
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• 2021

This study analyzed co-registration results according to the geometric processing level of reference image, which are Level 1R and Level 1G provided from KOMPSAT-3 and KOMPSAT-3A images. We performed co-registration using each Level 1R and Level 1G image as a reference image, and Level 1R image as a sensed image. For constructing the experimental dataset, seven Level 1R and 1G images of KOMPSAT-3 and KOMPSAT-3A acquired from Daejeon, South Korea, were used. To coarsely align the geometric position of the two images, SURF (Speeded-Up Robust Feature) and PC (Phase Correlation) methods were combined and then repeatedly applied to the overlapping region of the images. Then, we extracted tie-points using the SURF method from coarsely aligned images and performed fine co-registration through affine transformation and piecewise Linear transformation, respectively, constructed with the tie-points. As a result of the experiment, when Level 1G image was used as a reference image, a relatively large number of tie-points were extracted than Level 1R image. Also, in the case where the reference image is Level 1G image, the root mean square error of co-registration was 5 pixels less than the case of Level 1R image on average. We have shown from the experimental results that the co-registration performance can be affected by the geometric processing level related to the initial geometric relationship between the two images. Moreover, we confirmed that the better geometric quality of the reference image achieved the more stable co-registration performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.570-576
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• 2020

This study uses beacons and smartphone Global Positioning System (GPS) receivers to establish a location-based disaster/hazard prediction system. Beacons are usually installed indoors to locate users using triangulation in the room, but this study is differentiated from previous studies because the system is used outdoors to collect information on registration location and temperature and humidity in hazardous areas. In addition, since it is installed outdoors, waterproof, dehumidifying, and dustproof functions in the beacons themselves are required, and in case of heat and humidity, the sensor must be exposed to the outside, so the waterproof function is supplemented with a separate container. Based on these functions, information on declining and vulnerable areas is identified in real time, and temperature/humidity information is collected. We also propose a system that provides weather and fine-dust information for the area concerned. User location data are acquired through beacons and smartphone GPS receivers, and when users transmit from declining or vulnerable areas, they can establish the data to identify dangerous areas. In addition, temperature/humidity data in a microspace can be collected and utilized to build data to cope with climate change. Data can be used to identify specific areas of decline in a microspace, and various analyses can be made through the accumulated data.