• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.237-245
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• 2007

GB-SAR (Ground-Based Synthetic Aperture Radar) system is an imaging radar that obtains high resolution 2-D image through a synthetic aperture effect from the accurate linear-motion control of antenna on the ground. The highly versatile system configurations and accurate repeatability of GB-SAR operation allow one to accurately monitor the stability of surface scatterers with millimeter accuracy by SAR interferometry. In this paper we introduce the development of a GB-SAR system and show the possibilities of SAR polarimetry and interferometry such as DInSAR, Cross-Track InSAR, Delta-f InSAR, and PSInSAR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1858-1872
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• 2023

With the advance of radar technologies, the availability of synthetic aperture radar (SAR) images increases. To improve application of SAR images, a management system for SAR images is proposed in this paper. The system provides trainable land cover classification module and display of SAR images on the map. Users of the system can create their own classifier with their data, and obtain the classified results of newly captured SAR images by applying the classifier to the images. The classifier is based on convolutional neural network structure. Since there are differences among SAR images depending on capturing method and devices, a fixed classifier cannot cover all types of SAR land cover classification problems. Thus, it is adopted to create each user's classifier. In our experiments, it is shown that the module works well with two different SAR datasets. With this system, SAR data and land cover classification results are managed and easily displayed.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.319-328
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• 2024

Semi-supervised learning is a good way to train a classification model using a small number of labeled and large number of unlabeled data. We applied semi-supervised learning to a synthetic aperture radar(SAR) image classification model with a limited number of datasets that are difficult to create. To address the previous difficulties, semi-supervised learning uses a model trained with a small amount of labeled data to generate and learn pseudo labels. Besides, a lot of number of papers use a single fixed threshold to create pseudo labels. In this paper, we present a semi-supervised synthetic aperture radar(SAR) image classification method that applies different thresholds for each class instead of all classes sharing a fixed threshold to improve SAR classification performance with a small number of labeled datasets.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.501-504
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• 2000

The classical image reconstruction for stripmap SAR is the range-Doppler imaging. However, when the spotlight SAR system was envisioned, range-Bowler imaging fumed out to fail rapidly in this SAR imaging modality. What is referred to as polar format processing, which is based on the plane wave approximation, was introduced for imaging from spotlight SAR data. This paper has been studied for the raw data processing schemes in the spotlight-mode synthetic aperture radar. we apply the wavefront reconstruction scheme that does not utilize the approximation in spotlight-mode SAR imaging modelity, and compare the performance of target imaging with the polar format inversion scheme.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.1
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• pp.49-60
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• 2012

Synthetic aperture radar (SAR) has been employed in various fields due to its capability to generate high resolution images regardless of weather and visibility. This paper presents a performance analysis on the image formation of high resolution SAR according to various slant range distance and synthetic aperture lengths using a range migration algorithm simulator. Although the visual performance on the SAR image is more accurate, a numeric analysis resulted in a comparable measurement. More specifically, raw data were generated for an ideal point target upon imaging geometries and design parameters such as slant range distance and synthetic aperture lengths. Finally, spatial resolution, peak to sidelobe ratio and integrated sidelobe ratio are drawn to provide SAR capabilities in the initial concept design, final in-flight calibration and validation stages.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.130-138
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• 2003

The classical image reconstruction for stripmap-mode Synthetic Aperture Radar is the Range-Doppler algorithm. When the spotlight-mode SAR system was envisioned, Range-Doppler algorithm turned out to fail rapidly in this SAR imaging modality. Thus, what is referred to as Polar format algorithm, which is based on the Plane wave approximation, was introduced for imaging from spotlight-mode SAR raw- data. In this paper, we have studied for the raw data processing schemes in the spotlight-mode Synthetic Aperture Radar. We apply the Wavefront Reconstruction scheme that does not utilize the approximation in spotlight-mode SAR imaging modelity, and compare the performance of target imaging with the Polar format inversion scheme.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.89-95
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• 2017

Ship detection in synthetic aperture radar(SAR)imagery has long been an active research topic and has many applications. In this paper,we propose an efficient method for detecting ships from SAR imagery using filtering. This method exploits ship masking using a median filter that considers maximum ship sizes and detects ships from the reference image, to which a Non-Local means (NL-means) filter is applied for speckle de-noising and a differential image created from the difference between the reference image and the median filtered image. As the pixels of the ship in the SAR imagery have sufficiently higher values than the surrounding sea, the ship detection process is composed primarily of filtering based on this characteristic. The performance test for this method is validated using KOMPSAT-5 (Korea Multi-Purpose Satellite-5) SAR imagery. According to the accuracy assessment, the overall accuracy of the region that does not include land is 76.79%, and user accuracy is 71.31%. It is demonstrated that the proposed detection method is suitable to detect ships in SAR imagery and enables us to detect ships more easily and efficiently.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.8
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• pp.997-1004
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• 2007

High resolution synthetic aperture radar image could be sensitive to the various parameters of the payload, platform, and ground system. In this paper, a parameter based SAR simulator is presented for two-dimensional image formation and image quality analysis. Functional modules are implemented by Matalb code and GUI for the flexibility and expandability. Main function of this simulator includes the SAR input signal generation, range-doppler algorithm(RDA) based SAR image formation, and the SAR image quality analysis which is relevant to the SAR system design parameters. This simulator can effectively be used for the SAR image quality performance evaluation, which can be applicable to the airborne as well as spaceborne SAR system design and analysis.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1245-1254
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• 2023

Significant research has been conducted on the W-band synthetic aperture radar (SAR) system that utilizes the 77 GHz frequency modulation continuous wave (FMCW) radar. To reconstruct the high-resolution W-band SAR image, it is necessary to transform the point cloud acquired from the stereo cameras or the LiDAR in the direction of 6 degrees of freedom (DOF) and apply them to the SAR signal processing. However, there are difficulties in matching images due to the different geometric structures of images acquired from different sensors. In this study, we present the method to extract an optimized depth map by obtaining 6 DOF of the point cloud using a gradient descent method based on the entropy of the SAR image. An experiment was conducted to reconstruct a tree, which is a major road environment object, using the constructed W-band SAR system. The SAR image, reconstructed using the entropy-based gradient descent method, showed a decrease of 53.2828 in mean square error and an increase of 0.5529 in the structural similarity index, compared to SAR images reconstructed from radar coordinates.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.4
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• pp.392-400
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• 2021

Unlike optical equipment, SAR(Synthetic Aperture Radar) has the advantage of obtaining images in all weather, and object detection in SAR images is an important issue. Generally, deep learning-based object detection was mainly performed in real-valued network using only amplitude of SAR image. Since the SAR image is complex data consist of amplitude and phase data, a complex-valued network is required. In this paper, a complex-valued ResNet network is proposed. SAR image object detection was performed by combining the ROI transformer detector specialized for aerial image detection and the proposed complex-valued ResNet. It was confirmed that higher accuracy was obtained in complex-valued network than in existing real-valued network.