• Title/Summary/Keyword: SAR-SIFT

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Registration Method between High Resolution Optical and SAR Images (고해상도 광학영상과 SAR 영상 간 정합 기법)

  • Jeon, Hyeongju;Kim, Yongil
    • Korean Journal of Remote Sensing
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    • v.34 no.5
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    • pp.739-747
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    • 2018
  • Integration analysis of multi-sensor satellite images is becoming increasingly important. The first step in integration analysis is image registration between multi-sensor. SIFT (Scale Invariant Feature Transform) is a representative image registration method. However, optical image and SAR (Synthetic Aperture Radar) images are different from sensor attitude and radiation characteristics during acquisition, making it difficult to apply the conventional method, such as SIFT, because the radiometric characteristics between images are nonlinear. To overcome this limitation, we proposed a modified method that combines the SAR-SIFT method and shape descriptor vector DLSS(Dense Local Self-Similarity). We conducted an experiment using two pairs of Cosmo-SkyMed and KOMPSAT-2 images collected over Daejeon, Korea, an area with a high density of buildings. The proposed method extracted the correct matching points when compared to conventional methods, such as SIFT and SAR-SIFT. The method also gave quantitatively reasonable results for RMSE of 1.66m and 2.45m over the two pairs of images.

Comparison of Image Matching Method for Automatic Matching of High Resolution SAR Imagery (SAR 영상 자동정합을 위한 영상정합기법의 비교연구)

  • Baek, Sang Ho;Hong, Seung Hwan;Yoo, Su Hong;Sohn, Hong Gyoo
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.34 no.5
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    • pp.1639-1644
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    • 2014
  • SAR satellite can acquire clear imagery regardless of weather and the images are widely used for land management, natural hazard monitoring and many other applications. Automatic image matching technique is necessary for management of a huge amount of SAR data. Nevertheless, it is difficult to assure the accuracy of image matching due to the difference of image-capturing attitude and time. In this paper, we compared performances of MI method, FMT method and SIFT method by applying arbitrary displacement and rotation to TerraSAR-X images and changing resolution of the images. As a result, when the features having specific intensity were distributed well in SAR imagery, MI method could assure 0~2 pixels accuracy even if the images were captured in different geometry. But the accuracy of FMT method was significantly poor for the images having different spatial resolutions and the error was represented by tens or hundreds pixels. Moreover, the ratio of corresponding matching points for SIFT method was only 0~17% and it was difficult for SIFT method to apply to SAR images captured in different geometry.

Comparison of Multi-angle TerraSAR-X Staring Mode Image Registration Method through Coarse to Fine Step (Coarse to Fine 단계를 통한 TerraSAR-X Staring Mode 다중 관측각 영상 정합기법 비교 분석)

  • Lee, Dongjun;Kim, Sang-Wan
    • Korean Journal of Remote Sensing
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    • v.37 no.3
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    • pp.475-491
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    • 2021
  • With the recent increase in available high-resolution (< ~1 m) satellite SAR images, the demand for precise registration of SAR images is increasing in various fields including change detection. The registration between high-resolution SAR images acquired in different look angle is difficult due to speckle noise and geometric distortion caused by the characteristics of SAR images. In this study, registration is performed in two stages, coarse and fine, using the x-band SAR data imaged at staring spotlight mode of TerraSAR-X. For the coarse registration, a method combining the adaptive sampling method and SAR-SIFT (Scale Invariant Feature Transform) is applied, and three rigid methods (NCC: Normalized Cross Correlation, Phase Congruency-NCC, MI: Mutual Information) and one non-rigid (Gefolki: Geoscience extended Flow Optical Flow Lucas-Kanade Iterative), for the fine registration stage, was performed for performance comparison. The results were compared by using RMSE (Root Mean Square Error) and FSIM (Feature Similarity) index, and all rigid models showed poor results in all image combinations. It is confirmed that the rigid models have a large registration error in the rugged terrain area. As a result of applying the Gefolki algorithm, it was confirmed that the RMSE of Gefolki showed the best result as a 1~3 pixels, and the FSIM index also obtained a higher value than 0.02~0.03 compared to other rigid methods. It was confirmed that the mis-registration due to terrain effect could be sufficiently reduced by the Gefolki algorithm.

Augmented Reality Algorithm Selection Scheme for Military Multiple Image Analysis (국방용 다중 영상분석 증강현실 알고리즘 선택기술)

  • Yoo, Heouk-kyun;Chung, Jong-Moon
    • Journal of Internet Computing and Services
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    • v.20 no.4
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    • pp.55-61
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    • 2019
  • In this paper, if images are acquired in all-time situations through various sensors (EO/IR, SAR, GMTI, LiDAR) used for defense purposes, the images can be analyzed and expressed in augmented reality(AR). Various algorithms are used to process images with augmented reality, and depending on the situation, it is necessary to decide which algorithms to select and use. Through the performance comparison (error rate, processing time, accuracy) of SIFT, SURF, ORB, and BRISK, the representative augmented reality algorithm, it is analyzed and proposed which augmented reality algorithm is effective to use under various situations in the defense field.