• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.14-17
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• 2007

To make use of surveying data obtained from different sensors and different techniques in a common reference frame, it is a pre-requite step to register them in a common coordinate system. For this purpose, we have developed a methodology to register IKONOS-2 Satellite Imagery using ALS data. To achieve this, conjugate features from these data should be extracted in advance. In the study, linear features are chosen as conjugate features because they can be accurately extracted from man-made structures in urban area, and more easily than point features from ALS data. Then, observation equations are established from similarity measurements of the extracted features. During the process, considering the characteristics of systematic errors in IKONOS-2 satellite imagery, the transformation function were selected and used. In addition, we also analyzed how the number of linear features and their spatial distribution used as control features affect the accuracy of registration. Finally, the results were evaluated statistically and the results clearly demonstrated that the proposed algorithms are appropriate to register these data.

• 대한원격탐사학회지
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• 제26권3호
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• pp.325-333
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• 2010

Image registration involves overlapping images of an identical region and assigning the data into one coordinate system. Image registration has proved important in remote sensing, enabling registered satellite imagery to be used in various applications such as image fusion, change detection and the generation of digital maps. The image descriptor, which extracts matching points from each image, is necessary for automatic registration of remotely sensed data. Using contrast enhancement algorithms such as histogram equalization and image stretching, the normalized data are applied to the image descriptor. Drawing on the different spectral characteristics of high resolution satellite imagery based on sensor type and acquisition date, the applied normalization method can be used to change the results of matching interest point descriptors. In this paper, the matching points by scale invariant feature transformation (SIFT) are extracted using various contrast enhancement algorithms and injection of Gaussian noise. The results of the extracted matching points are compared with the number of correct matching points and matching rates for each point.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.641-644
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• 2005

This paper introduces an Interactive Feature Extraction (!FE) approach for the registration of satellite imagery by matching extracted point and line features. !FE method contains both point extraction by cross-correlation matching of singular points and line extraction by Hough transform. The purpose of this study is to minimize user's intervention in feature extraction and easily apply the extracted features for image registration. Experiments with these imagery dataset proved the feasibility and the efficiency of the suggested method.

• ETRI Journal
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• 제28권2호
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• pp.162-174
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• 2006

Several photogrammetric and geographic information system applications such as surface matching, object recognition, city modeling, environmental monitoring, and change detection deal with multiple versions of the same surface that have been derived from different sources and/or at different times. Surface registration is a necessary procedure prior to the manipulation of these 3D datasets. This need is also applicable in the field of medical imaging, where imaging modalities such as magnetic resonance imaging (MRI) can provide temporal 3D imagery for monitoring disease progression. This paper will present a general automated surface registration procedure that can establish correspondences between conjugate surface elements. Experimental results using light detection and ranging (LIDAR) and MRI data will verify the feasibility, robustness, and accuracy of this approach.

• 대한공간정보학회지
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• 제21권4호
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• pp.101-107
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• 2013

일반적으로 상용화되고 있는 고해상도 위성영상에는 좌표가 부여되어 있지만, 촬영 당시 센서의 자세나 지표면 특성 등에 따라서 영상 간의 지역적인 위치차이가 발생한다. 따라서 좌표를 일치시켜주는 영상 간 상호등록 과정이 필수적으로 적용되어야 한다. 하지만 영상 내에 구름이 분포할 경우 두 영상 간의 정합쌍을 추출하는데 어려움을 주며, 오정합쌍을 다수 추출하는 경향을 보인다. 이에 본 연구에서는 구름이 포함된 고해상도 KOMPSAT-2 영상간의 자동 기하보정을 수행하기 위한 방법론을 제안한다. 대표적인 특징기반 정합쌍 추출 기법인 SIFT 기법을 이용하였고, 기준영상의 특징점을 기준으로 원형 버퍼를 생성하여, 오직 버퍼 내에 존재하는 대상영상의 특징점만을 후보정합쌍으로 선정하여 정합률을 높이고자 하였다. 제안 기법을 구름이 포함된 다양한 실험지역에 적용한 결과, SIFT 기법에 비해 높은 정합률을 보였고, 상호등록 정확도를 향상시킴을 확인할 수 있었다.

• 정보과학회 논문지
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• 제43권9호
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• pp.1034-1042
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• 2016

영역기반 영상정합은 미리 정의된 특징의 도움 없이 영상을 정합할 수 있기 때문에, 기계학습과 접목된다면 이론 상 다양한 영상정합 문제에 적용 가능하다. 그러나 신속한 정합을 위하여, 미리 정의된 특징을 탐지하여 패치 쌍 후보를 선정에 사용하는데, 이는 영역기반 방법의 적용성에 제약을 준다. 이를 해소하기 위하여 본 연구에서는 단순히 두 패치의 관련도 뿐만 아니라 두 패치가 어느 정도 공간 상 떨어져 있는지에 대한 정보를 제공하는 ConvNet Dart를 개발하였다. 이러한 정보를 기반으로 효율적으로 패치 쌍 탐색공간을 줄일 수 있었다. 추가로 Dart가 제대로 작동할 수 없는 영역을 식별하는 ConvNet Fad를 개발하여 정합의 정밀도를 높였다. 본 연구에서는 이들을 딥러닝으로 학습하였으며, 이를 위해 소수의 정합된 영상에서 다량의 예제를 생성하는 방법을 개발하였다. 마지막으로 단순한 영상정합 문제에 성공적으로 적용하여, 이러한 방법론이 작동하는 것을 보였다.

• 대한원격탐사학회지
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• 제27권1호
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• pp.9-15
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• 2011

푸쉬브룸 스캐너 PAN영상과 MS영상 사이에는 오프셋이 존재하며 서로 다른 시간과 각도로 촬영하고 있다. 이로 인하여 구름과 같이 빠르게 움직이는 물체는 오정합 점들을 생성하며 이는 PAN영상과 MS영상간의 상호영상등록의 오차를 발생시킨다. 특히 구름(안개 및 스모그 포함)이 있는 기상조건 하에서 얻어진 위성영상은 구름에 의해 가려진 지형정보를 추출하는 데 있어 많은 문제를 야기하기 때문에 정확한 영상등록을 위해서는 효과적인 구름 탐지 및 제거 알고리즘이 필요하다. 구름 제거를 위한 관련 연구들은 크게 다음과 같은 세 가지로 나누어지는데 (1) 구름 검출 알고리즘을 통해 구름으로 여겨지는 영역을 분리하여 구름영역을 제거하는 방법 (2) 다중분광영상의 밴드정보를 이용하는 방법 (3) 다시기 영상정보를 이용하는 방법들로 나눌 수 있다. 본 연구에서는 구름 지역을 제거하는 방법과 다시기영상을 이용하는 방법을 사용하여 구름이 포함된 푸쉬브룸 스캐너 밴드간 영상등록의 정확도를 비교, 분석하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.594-597
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• 2006

Image co-registration is the process of overlaying two images of the same scene. One of which is a reference image, while the other (sensed image) is geometrically transformed to the one. Numerous methods were developed for the automated image co-registration and it is known as a time-consuming and/or computation-intensive procedure. In order to improve efficiency and effectiveness of the co-registration of satellite imagery, this paper proposes a pre-qualified area matching, which is composed of feature extraction with Laplacian filter and area matching algorithm using correlation coefficient. Moreover, to improve the accuracy of co-registration, the outliers in the initial matching point should be removed. For this, two outlier detection techniques of studentized residual and modified RANSAC algorithm are used in this study. Three pairs of Landsat images were used for performance test, and the results were compared and evaluated in terms of robustness and efficiency.

• 대한원격탐사학회지
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• 제22권5호
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• pp.439-443
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• 2006

Image co-registration is the process of overlaying two images of the same scene. One of which is a reference image, while the other (sensed image) is geometrically transformed to the one. Numerous methods were developed for the automated image co-registration and it is known as a timeconsuming and/or computation-intensive procedure. In order to improve efficiency and effectiveness of the co-registration of satellite imagery, this paper proposes a pre-qualified area matching, which is composed of feature extraction with Laplacian filter and area matching algorithm using correlation coefficient. Moreover, to improve the accuracy of co-registration, the outliers in the initial matching point should be removed. For this, two outlier detection techniques of studentized residual and modified RANSAC algorithm are used in this study. Three pairs of Landsat images were used for performance test, and the results were compared and evaluated in terms of robustness and efficiency.

• 대한원격탐사학회지
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• 제25권3호
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• pp.287-294
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• 2009

KOMPSAT-2 like other high-resolution satellites has the time and angle difference in the acquisition of the panchromatic (PAN) and multispectral (MS) images because the imaging systems have the offset of the charge coupled device combination in the focal plane. Due to the differences, high altitude and moving objects, such as clouds, have a different position between the PAN and MS images. Therefore, a mis-registration between the PAN and MS images occurs when a registration algorithm extracted matching points from these cloud objects. To overcome this problem, we proposed a new registration method. The main idea is to discard the matching points extracted from cloud boundaries by using an automatic thresholding technique and a classification technique on a distance disparity map of the matching points. The experimental result demonstrates the accuracy of the proposed method at ground region around cloud objects is higher than a general method which does not consider cloud objects. To evaluate the proposed method, we use KOMPSAT-2 cloudy images.