Accurate Geometric Correction Research Articles

A Novel Multi-Projection Correction Method Based on Binocular Vision

In order to improve the accuracy of multi-projection correction fusion, a multi-projection correction method based on binocular vision is proposed. To date, most of the existing methods are based on the single-camera mode, which may lose the depth information of the display wall and may not accurately obtain the details of the geometric structure of the display wall. The proposed method uses the depth information of a binocular camera to build a high-precision 3D display wall model; thus, there is no need to know the specific CAD size of the display wall in advance. Meanwhile, this method can be applied to display walls of any shape. By calibrating the binocular vision camera, the radial and eccentric aberration of the camera can be reduced. The projector projects the encoded structured light stripes, and the high-precision 3D structural information of the projection screen can be reconstructed according to the phase relationship after collecting the deformation stripes on the projection screen with the binocular camera. Thus, the screen-projector sub-pixel-level mapping relationship is established, and a high-precision geometric correction is achieved. In addition, by means of the one-to-one mapping relation between the phase information and the three-dimensional space points, accurate point cloud matching among multiple binocular phase sets could be established, so that such method can be applied to any quantity of projectors. The experimental results based on various special-shaped projection screens show that, comparing to the single-camera-based method, the proposed method improves the geometric correction accuracy of multi-projection stitching by more than 20%. This method also has many advantages, such as strong universality, high measurement accuracy, and rapid measurement speed, which indicate its wide application potential in many different fields.

Creation of a Virtual Tour .Exe Utilizing Very High-Resolution RGB UAV Data

In the last decades, developments in game engine technology led to a raised attraction to the virtual reality (VR) and augmented reality (AR) concepts which offer users an interactive synthetic environment. Also, with the travel limitations of the current COVID-19 pandemic, VR tour applications that visualize the geospatial data gained popularity more than ever. In this study, a three-dimensional (3D) VR tour application was created for Gebze Technical University (GTU) Campus by integrating unmanned aerial vehicle (UAV) data into an artificial environment by using cross-platform game development engine Unity. For creating high-quality 3D models of the Campus, different imaging geometries and flight altitudes were applied. The aerial photos were achieved with a ground sampling distance (GSD) of ≤2.2 cm with a 20 megapixel (MP) Sony Exmor RGB camera. Point cloud processing and the generation of high-quality 3D products were carried out by structure from motion (SfM) based photogrammetric software Agisoft Metashape. Using 86 well-distributed ground control points (GCPs), geometric correction accuracy of ±2 cm (~0.9 pixels) was reached as root mean square error (RMSE). Generated 3D models were imported into the Unity environment and the negative influence of high polygon data on the application performance was reduced by applying occlusion culling and space subdivision rendering optimization algorithms. The visual potential of the VR was improved by adding 3D individual object models such as trees, benches and arbors. For enhancing the information content of the VR tour, interactive information panels including the building metadata such as building name, block name and total floor area were placed. Finally, a first-person player was implemented for a realistic VR experience.

Improving Imaging of the Brainstem and Cerebellum in Autistic Children: Transformation-Based High-Resolution Diffusion MRI (TiDi-Fused) in the Human Brainstem.

Diffusion-weighted magnetic resonance imaging (dMRI) of the brainstem is technically challenging, especially in young autistic children as nearby tissue-air interfaces and motion (voluntary and physiological) can lead to artifacts. This limits the availability of high-resolution images, which are desirable for improving the ability to study brainstem structures. Furthermore, inherently low signal-to-noise ratios, geometric distortions, and sensitivity to motion not related to molecular diffusion have resulted in limited techniques for high-resolution data acquisition compared to other modalities such as T1-weighted imaging. Here, we implement a method for achieving increased apparent spatial resolution in pediatric dMRI that hinges on accurate geometric distortion correction and on high fidelity within subject image registration between dMRI and magnetization prepared rapid acquisition gradient echo (MPnRAGE) images. We call this post-processing pipeline T1 weighted-diffusion fused, or “TiDi-Fused”. Data used in this work consists of dMRI data (2.4 mm resolution, corrected using FSL’s Topup) and T1-weighted (T1w) MPnRAGE anatomical data (1 mm resolution) acquired from 128 autistic and non-autistic children (ages 6–10 years old). Accurate correction of geometric distortion permitted for a further increase in apparent resolution of the dMRI scan via boundary-based registration to the MPnRAGE T1w. Estimation of fiber orientation distributions and further analyses were carried out in the T1w space. Data processed with the TiDi-Fused method were qualitatively and quantitatively compared to data processed with conventional dMRI processing methods. Results show the advantages of the TiDi-Fused pipeline including sharper brainstem gray-white matter tissue contrast, improved inter-subject spatial alignment for group analyses of dMRI based measures, accurate spatial alignment with histology-based imaging of the brainstem, reduced variability in brainstem-cerebellar white matter tracts, and more robust biologically plausible relationships between age and brainstem-cerebellar white matter tracts. Overall, this work identifies a promising pipeline for achieving high-resolution imaging of brainstem structures in pediatric and clinical populations who may not be able to endure long scan times. This pipeline may serve as a gateway for feasibly elucidating brainstem contributions to autism and other conditions.

Correction of Very High-Resolution Satellite Images using Control Points Captured by Web Map Service (WMS) server: Google Earth

The recent progress for spatial resolution of remote sensing imagery led to generate many types of Very High-Resolution (VHR) satellite images, consequently, general speaking, it is possible to prepare accurate base map larger than 1:10,000 scale. One of these VHR satellite image is WorldView-3 sensor that launched in August 2014. The resolution of 0.31m makes WorldView-3 the highest resolution commercial satellite in the world. In the current research, a pan-sharpen image from that type, covering an area at Giza Governorate in Egypt, used to determine the suitable large-scale map that could be produced from that image. To reach this objective, two different sources for acquiring Ground Control Points (GCPs). Firstly, very accurate field measurements using GPS and secondly, Web Map Service (WMS) server (in the current research is Google Earth) which is considered a good alternative when GCPs are not available, are used. Accordingly, three scenarios are tested, using the same set of both 16 Ground Control Points (GCPs) as well as 14 Check Points (CHKs), used for evaluation the accuracy of geometric correction of that type of images. First approach using both GCPs and CHKs coordinates acquired by GPS. Second approach using GCPs coordinates acquired by Google Earth and CHKs acquired by GPS. Third approach using GCPs and CHKs coordinates by Google Earth. Results showed that, first approach gives Root Mean Square Error (RMSE) planimeteric discrepancy for GCPs of 0.45m and RMSE planimeteric discrepancy for CHKs of 0.69m. Second approach gives RMSE for GCPs of 1.10m and RMSE for CHKs of 1.75m. Third approach gives RMSE for GCPs of 1.10m and RMSE for CHKs of 1.40m. Taking map accuracy specification of 0.5mm of map scale, the worst values for CHKs points (1.75m&1,4m) resulted from using Google Earth as a source, gives the possibility of producing 1:5000 large-scale map compared with the best value of (0.69m) (map scale 1:2500). This means, for the given parameters of the current research, large scale maps could be produced using Google Earth, in case of GCPs are not available accurately from the field surveying, which is very useful for many users.

Discrete-binary configuration of metaheuristic algorithms for RFM optimization in the case of limited GCPs

Metaheuristic algorithms have been widely used in determining optimum rational polynomial coefficients (RPCs). By eliminating a number of unnecessary RPCs, these algorithms increase the accuracy of geometric correction of high-resolution satellite images. To this end, these algorithms use ordinary least squares and a number of ground control points (GCPs) to estimate RPCs. Due to the cost of GCP collection, using limited GCPs has become an attractive topic in various research studies. A configuration for RPC estimation using metaheuristic algorithms, namely, discrete-binary configuration for rational function model (DBRFM), is presented to find the optimal number and combination of RPCs in the case of limited GCPs. Based on the fact that the maximum number of RPCs is twice the number of GCPs, the particle/chromosome in the proposed configuration is composed of two binary and discrete parts. This configuration not only is compatible with the nature of the metaheuristic algorithms but also significantly reduces the search space. The proposed method has been tested on various types of remotely sensed data sets. The results of the experiments indicate the superiority of the DBRFM in comparison with the traditional binary configuration of metaheuristic algorithms.

Impact of prospective motion correction, distortion correction methods and large vein bias on the spatial accuracy of cortical laminar fMRI at 9.4 Tesla

Functional imaging with sub-millimeter spatial resolution is a basic requirement for assessing functional MRI (fMRI) responses across different cortical depths and is used extensively in the emerging field of laminar fMRI. Such studies seek to investigate the detailed functional organization of the brain and may develop to a new powerful tool for human neuroscience. However, several studies have shown that measurement of laminar fMRI responses can be biased by the image acquisition and data processing strategies. In this work, measurements with three different gradient-echo EPI BOLD fMRI protocols with a voxel size down to 650 ​μm isotropic were performed at 9.4 ​T. We estimated how prospective motion correction can help to improve spatial accuracy by reducing the number of spatial resampling steps in postprocessing. In addition, we demonstrate key requirements for accurate geometric distortion correction to ensure that distortion correction maps are properly aligned to the functional data and that strong variations of distortions near large veins can lead to signal overlays which cannot be corrected for during postprocessing. Furthermore, this study illustrates the spatial extent of bias induced by pial and other larger veins in laminar BOLD experiments. Since these issues under investigation affect studies performed with more conventional spatial resolutions, the methods applied in this work may also help to improve the understanding of the BOLD signal more broadly.

A Multitemporal Remote Sensing Image Registration Method Based on Water Bodies for the Lake-Rich Region

Accurate multitemporal remotely sensed image registration is essential for water body change detection and analysis of lake dynamics. However, lake-rich regions, such as Siberia, are mostly dominated by rivers and lakes, with few stable geometry features such as those typically used as control points for image registration. Lakes in Arctic regions are generally not static; their shorelines tend to expand and shrink seasonally, and may change substantially between different years, making it difficult to find consistent features for image registration. Consequently, traditional image-to-image registration methods, and even many sophisticated registration algorithms, rarely achieve accurate geometric correction in Arctic regions due to a lack of sufficient control points. In this article, we proposed a summary lake spatial attribute, the inferred deepest point of the lake (DPL), as a feature that is relatively insensitive to lake area changes, and therefore useful for registration of multitemporal images. The central focus of the DPL estimation algorithm is the identification of the largest inner circle (LIC) of the lake polygon. First, the internal Voronoi diagram of a lake polygon is calculated by the ``divide-and-conquer” method. The medial axis (MA) is then calculated by Voronoi diagram simplification, and finally, the LIC center is obtained by computing the distance from the MA intersection to all polygon edges. The approach was used to register HJ-1 and Landsat multispectral scanner (MSS) images in Siberia, where water bodies dominate the landscape and change significantly over time. The proposed method found a large number of control points from the extracted water bodies. Subpixel registration accuracy of 0.62 pixels (18.5 m) and 0.33 pixels (19.6 m) root mean square error (RMSE) was obtained for the HJ-1 and MSS images, respectively. In comparison, the alternative method of using lake centroids, only achieved 0.75 pixels (22.4 m) and 0.49 pixels (29.3 m) RMSE. This registration accuracy improvement is potentially important for large-scale regional cartography and change detection applications.

Multi-Projector Calibration Based on Virtual Viewing Space

In large-scale maritime maneuvering simulators, the multi-channel systems are usually used to tile multiple projectors on a cylindrical display to create a large virtual environment, which brings more realistic immersion to the users. In the non-planar multi-projector display system, to finish the work of geometric and photometric corrections, there exist the problems of difficult projector calibration, more manual intervention and poor adaptability. In order to solve these problems, a multi-projector calibration based on virtual viewing space is proposed in this paper. First, a virtual environment of multiple projectors is created, and by using the structured light and re-projection model on a cylindrical wall, the calibration of internal and external parameters of a projector are realized separately. Finally, the geometric correction is completed by the mapping relationship between the target images and the calibrated re-projected images. This method is free of limitation by the angle of view of a single camera and can calibrate any of the projectors in the multi-channel systems. The example results show that the projector’s parameters can be estimated more accurately, and the efficiency and accuracy of geometric correction can be improved. Good results are achieved when applying this method in the maritime maneuvering simulator system.

Assessment of geometric correction of remote sensing satellite images using RPC versus GCP

Geometric distortions are common problems when dealing with remote sensing satellite images. Therefore, geometric correction is a necessary process for preparing remote sensing satellite images for many applications. Physical sensor model is formed by integrating the geometry of imaging sensor and positioning sensors as GPS and star trackers with system calibration parameters. Physical sensor model is not available in common but a Rational Polynomial Coefficient (RPC) model is provided as an alternative representation of sensor model. The RPC model is used for geometric correction of satellite image to get the spatial data of image features. The accuracy of the resultant image depends on the accuracy of RPC model which is not known as common. The research objective is to assess the accuracy of geometric correction of the satellite image using RPC model versus geometric correction using Ground Control Points (GCPs), along with their effect on the final accuracy of the output spatial features. The available data is IKONOS-2 image with its RPC file and seven GCPs with high accuracy obtained from ground survey for the study area. The input image is corrected using two available data separately. First degree of polynomial is used for transformation process for the case of GCPs. Bilinear interpolation technique is used to determine the pixel value of the newly resultant corrected images for the two cases. GCPs is preferred when available because the resultant image with RPC geometric correction has 15.0 meters average linear error while 3.0 meters error in case of GCPs geometric correction.

Overview of Akatsuki data products: definition of data levels, method and accuracy of geometric correction

We provide an overview of data products from observations by the Japanese Venus Climate Orbiter, Akatsuki, and describe the definition and content of each data-processing level. Levels 1 and 2 consist of non-calibrated and calibrated radiance (or brightness temperature), respectively, as well as geometry information (e.g., illumination angles). Level 3 data are global-grid data in the regular longitude–latitude coordinate system, produced from the contents of Level 2. Non-negligible errors in navigational data and instrumental alignment can result in serious errors in the geometry calculations. Such errors cause mismapping of the data and lead to inconsistencies between radiances and illumination angles, along with errors in cloud-motion vectors. Thus, we carefully correct the boresight pointing of each camera by fitting an ellipse to the observed Venusian limb to provide improved longitude–latitude maps for Level 3 products, if possible. The accuracy of the pointing correction is also estimated statistically by simulating observed limb distributions. The results show that our algorithm successfully corrects instrumental pointing and will enable a variety of studies on the Venusian atmosphere using Akatsuki data.

Correction of metal-induced susceptibility artifacts for functional MRI during deep brain stimulation

Functional magnetic resonance imaging (fMRI) is an emerging tool for investigating brain activation associated with, or modulated by, deep brain stimulation (DBS). However, DBS-fMRI generally suffers from severe susceptibility to artifacts in regions near the metallic stimulation electrodes, as well as near tissue/air boundaries of the brain. These result in strong intensity and geometric distortions along the phase-encoding (PE) (i.e., blipped) direction in gradient-echo echo-planar imaging (GE-EPI). Distortion presents a major challenge to conducting reliable data analysis and in interpreting the findings. A recent study showed that the point spread function (PSF) mapping-based reverse gradient approach has a potential to correct for distortions not only in spin-echo EPI, but also in GE-EPI acquired in both the forward and reverse PE directions. In this study, we adapted that approach in order to minimize severe metal-induced susceptibility artifacts for DBS-fMRI, and to evaluate the performance of the approach in a phantom study and a large animal DBS-fMRI study. The method combines the distortion-corrected GE-EPI pair with geometrically different intensity distortions due to the opposing encoding directions. The results demonstrate that the approach can minimize susceptibility artifacts that appear around the metallic electrodes, as well as in the regions near the tissue/air boundaries in the brain. We also demonstrated that an accurate geometric correction is important in improving BOLD contrast in the group dataset, especially in regions where strong susceptibility artifacts appear.

A NEW CONTROL POINTS BASED GEOMETRIC CORRECTION ALGORITHM FOR AIRBORNE PUSH BROOM SCANNER IMAGES WITHOUT ON-BOARD DATA

Push broom scanners, such as video spectrometers (also called hyperspectral sensors), are widely used in the present. Usage of scanned images requires accurate geometric correction, which becomes complicated when imaging platform is airborne. This work contains detailed description of a new algorithm developed for processing of such images. The algorithm requires only user provided control points and is able to correct distortions caused by yaw, flight speed and height changes. It was tested on two series of airborne images and yielded RMS error values on the order of 7 meters (3-6 source image pixels) as compared to 13 meters for polynomial-based correction.

A NEW CONTROL POINTS BASED GEOMETRIC CORRECTION ALGORITHM FOR AIRBORNE PUSH BROOM SCANNER IMAGES WITHOUT ON-BOARD DATA

Abstract. Push broom scanners, such as video spectrometers (also called hyperspectral sensors), are widely used in the present. Usage of scanned images requires accurate geometric correction, which becomes complicated when imaging platform is airborne. This work contains detailed description of a new algorithm developed for processing of such images. The algorithm requires only user provided control points and is able to correct distortions caused by yaw, flight speed and height changes. It was tested on two series of airborne images and yielded RMS error values on the order of 7 meters (3-6 source image pixels) as compared to 13 meters for polynomial-based correction.

Geometric correction of APEX hyperspectral data

Abstract Hyperspectral imagery originating from airborne sensors is nowadays widely used for the detailed characterization of land surface. The correct mapping of the pixel positions to ground locations largely contributes to the success of the applications. Accurate geometric correction, also referred to as “orthorectification”, is thus an important prerequisite which must be performed prior to using airborne imagery for evaluations like change detection, or mapping or overlaying the imagery with existing data sets or maps. A so-called “ortho-image” provides an accurate representation of the earth’s surface, having been adjusted for lens distortions, camera tilt and topographic relief. In this paper, we describe the different steps in the geometric correction process of APEX hyperspectral data, as applied in the Central Data Processing Center (CDPC) at the Flemish Institute for Technological Research (VITO, Mol, Belgium). APEX ortho-images are generated through direct georeferencing of the raw images, thereby making use of sensor interior and exterior orientation data, boresight calibration data and elevation data. They can be referenced to any userspecified output projection system and can be resampled to any output pixel size.

The georeferencing of RASAT satellite imagery and some practical approaches to increase the georeferencing accuracy

RASAT Earth Observation Satellite is the second remote sensing satellite of The Scientific and Technological Research Council of Turkey (TUBITAK) Space Technologies Search Institute (TUBITAK Space). Generally, the first step to utilize the satellite imagery in GIS applications is the accurate geometric correction of the imagery. But, the geometric correction process of RASAT images is somewhat difficult due to insufficient orbit data and lack of satellite imagery processing software with RASAT model. Although the geolocation of RASAT images is investigated in some recent studies, subpixel accuracies cannot be achieved. In this study, different geometric correction methods and combination of them are tested with a more interactive workflow that uses the results of other approaches. Results show that these approaches can be used for the geometric correction of RASAT like pushbroom satellite images with insufficient orbit data and better geolocation accuracies can be achieved by different geometric correction approaches.

Automated geometric correction of multispectral images from High Resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B

China–Brazil Earth Resource Satellite (CBERS) imagery is identified as one of the potential data sources for monitoring Earth surface dynamics in the event of a Landsat data gap. Currently available multispectral images from the High Resolution CCD (Charge Coupled Device) Camera (HRCC) on-board CBERS satellites (CBERS-2 and CBERS-2B) are not precisely geo-referenced and orthorectified. The geometric accuracy of the HRCC multispectral image product is found to be within 2–11km. The use of CBERS-HRCC multispectral images to monitor Earth surface dynamics therefore necessitates accurate geometric correction of these images. This paper presents an automated method for geo-referencing and orthorectifying the multispectral images from the HRCC imager on-board CBERS satellites. Landsat Thematic Mapper (TM) Level 1T (L1T) imagery provided by the U.S. Geological Survey (USGS) is employed as reference for geometric correction. The proposed method introduces geometric distortions in the reference image prior to registering it with the CBERS-HRCC image. The performance of the geometric correction method was quantitatively evaluated using a total of 100images acquired over the Andes Mountains and the Amazon rainforest, two areas in South America representing vastly different landscapes. The geometrically corrected HRCC images have an average geometric accuracy of 17.04m (CBERS-2) and 16.34m (CBERS-2B). While the applicability of the method for attaining sub-pixel geometric accuracy is demonstrated here using selected images, it has potential for accurate geometric correction of the entire archive of CBERS-HRCC multispectral images.

Automated Geometric Correction of Landsat MSS L1G Imagery

Multispectral scanners (MSS) onboard Landsat satellites 1-5 provide the earliest record of Earth observation data. Currently available Landsat MSS data products are processed by the U.S. Geological Survey (USGS) using the Level 1 Product Generation System (LPGS) to either L1T or L1G level of correction. MSS images processed to L1T have higher geometric accuracy since the images are precisely geo-referenced and orthorectified. The L1G MSS image product is available in cases when the elevation data and ground control information necessary to perform L1T correction are not available. The use of L1G MSS products for multitemporal change analysis necessitates accurate geometric correction of these images. This letter presents an automated method for accurate geo-referencing and orthorectification of L1G MSS images. The method is used to geometrically correct L1G MSS images acquired over South Dakota and the Himalayas, two areas representing vastly different landscapes. The performance of the method is evaluated quantitatively, and an average geometric accuracy of 0.47 pixel at MSS resolution is achieved. While the applicability of this method is demonstrated here using selected L1G MSS images, it has potential for accurate geometric correction of the entire archive of Landsat L1G MSS images.

A New Time Series Mining Approach Applied to Multitemporal Remote Sensing Imagery

In this paper, we present a novel unsupervised algorithm, called CLimate and rEmote sensing Association patteRns Miner, for mining association patterns on heterogeneous time series from climate and remote sensing data integrated in a remote sensing information system developed to improve the monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing images, an image preprocessing module, a time series extraction module, and time series mining methods. The preprocessing module was projected to perform accurate geometric correction, what is a requirement particularly for land and agriculture applications of satellite images. The time series extraction is accomplished through a graphical interface that allows easy interaction and high flexibility to users. The time series mining method transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in other series within a temporal sliding window. The validation process was achieved with agroclimatic data and NOAA-AVHRR images of sugar cane fields. Results show a correlation between agroclimatic time series and vegetation index images. Rules generated by our new algorithm show the association patterns in different periods of time in each time series, pointing to a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast without having the burden of dealing with many data charts.

An Optimization Method on Ground Control Point Distribution

With the development of remote sensing, the data quantity of remote sensing image is increasing tremendously. It brings a huge workload to the image geometric correction through manual ground control point (GCP) selection. GCPs automatically selected based on software is one of the effective methods to cut down manual operation. The GCPs obtained from that way is generally redundant. This paper deeply comprehends some existing methods about automatic optimization of GCP, and puts forward a new method of automatic optimization of GCP based on Thiessen Polygon to filter ground control points from the overfull ones without manual subjectivity for better accuracy. Experiments in this paper also demonstrated the relationship between the accuracy of geometric correction and the distribution of GCP. It advances the conception of single GCP’s importance value based on Thiessen Polygon. The paper gives the theory and the flow of automatic optimization of GCPs as well. It also presents an example of the application of this method. In the conclusion, this paper points out the advantages of this method .

선형정보를 이용한 고해상도 광학영상과 SAR 영상 간 기하보정

다중센서자료를 동시에 활용하기 위해서는 센서 간의 정밀한 기하보정이 요구된다. 이에 본 연구에서는 선형정보를 추출하여 고해상도의 광학영상과 SAR 영상 간의 기하보정을 수행하는 것을 목적으로 한다. 이를 위해 기준영상과 대상영상에 대하여 수동으로 매칭쌍을 추출하여 두 영상 간의 좌표체계를 개략적으로 일치시켜주는 과정을 전처리로 수행하였다. 방사적 특성이 다른 두 영상에 대하여 Canny edge operator를 통해 선형 화소를 추출한 뒤, 비용함수를 구성하여 유사하다고 생각되는 점을 초기 매칭쌍으로 선정하고, 그 중에서 이상치로 판단되는 오매칭쌍을 제거하고 남은 대상을 최종 매칭쌍으로 추출하였다. 본 기법을 통해 영상 전역에 걸쳐서 고르게 분포된 다수의 매칭쌍을 추출할 수 있었을 뿐만 아니라, 고도가 높거나 고도 변화가 큰 지역적 특성으로 인해 지리적 위치오차를 포함하는 지역에서 추출된 매칭쌍을 효과적으로 제거할 수 있었다. 최종적으로 추출된 매칭쌍을 이용하여 piecewise linear function과 affine transformation을 결합한 새로운 변환모델식을 적용하여 기하보정 정확도를 높이고자 하였고, 수동으로 추출된 검사점을 통해 1.58의 RMSE 값을 도출하였다. Precise image-to-image registration is required to process multi-sensor data together. The purpose of this paper is to develop an algorithm that register between high-resolution optical and SAR images using linear features. As a pre-processing step, initial alignment was fulfilled using manually selected tie points to remove any dislocations caused by scale difference, rotation, and translation of images. Canny edge operator was applied to both images to extract linear features. These features were used to design a cost function that finds matching points based on their similarity. Outliers having larger geometric differences than general matching points were eliminated. The remaining points were used to construct a new transformation model, which was combined the piecewise linear function with the global affine transformation, and applied to increase the accuracy of geometric correction.