• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.5
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• pp.680-687
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• 2014

PGA(phase gradient autofocus) has been widely used to remove motion induced phase errors in the ISAR(inverse synthetic aperture radar) imaging. The critical process for the processing time and image quality is windowing stage in PGA. In this paper, the new method to determine window size based on polynomial least square approximation is proposed. Moreover, dominant range bins are selected for efficient phase error estimation, which improve image quality and speed up convergence. The simulation results show that the proposed algorithm provides high quality ISAR images while computational efficiency of inherent PGA is retained.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.3
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• pp.195-207
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• 2017

Recently, the SAR-GMTI mode is becoming increasingly essential in airborne radar systems. While SAR requires wideband waveforms for high resolution imaging, GMTI requires narrowband waveforms for doppler processing, which makes general LFM waveforms difficult to use for SAR-GMTI. This paper analyses the FJB(Frequency Jump Burst) waveform, which is studied for the SAR-GMTI waveform, and presents the method for the pulse compression and SAR image formation using FJB waveforms. Simulation results show that there is little difference in performances between the FJB waveform and the LFM waveform.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.7-12
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• 2001

In this paper, an estimation method of hybrid feature was proposed to detect linear feature such as the road network from SAR(synthetics aperture radar) images that include speckle noise. First we considered the mean intensity ratio or the statistical properties of locality neighboring regions to detect linear feature of road. The responses of both methods are combined to detect the entire road network. The purpose of this paper is to extract the segments of road and to mutually connect them according to the identical intensity road from the locally detected fusing images. The algorithm proposed in this paper is to define MRF(markov random field) model of the priori knowledge on the roads and applied it to energy function of interacting density points, and to detect the road networks by optimizing the energy function.

• Journal of IKEEE
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• v.24 no.4
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• pp.1034-1038
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• 2020

In order to effectively process target recognition using radar, accurate signal information for the target is required. However, such a target signal is usually mixed with noise, and this part of the study is continuously carried out. Especially, image processing, target signal processing and target recognition for the target are examples. Since the field of target recognition is important from a military point of view, this paper carried out research on target recognition of aircraft using a tree-structured fuzzy neural networks. Fuzzy neural networks are learned by using reflected signal data for an aircraft to optimize the model, and then test data for the target are used for the optimized model to perform an experiment on target recognition. The effectiveness of the proposed method is verified by the simulation results.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1357-1369
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• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1935-1943
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• 2022

The development and application of a high-resolution soil moisture mapping method using satellite imagery has been considered one of the major research themes in remote sensing. In this study, soil moisture mapping in the test area of Jeju Island was performed. The soil moisture was calculated with optical images using linearly adjusted Synthetic Aperture Radar (SAR) polarization images and incident angle. SAR Backscatter data, Analysis Ready Data (ARD) provided by Google Earth Engine (GEE), was used. In the soil moisture processing process, the optical image was applied to normalized difference vegetation index (NDVI) by surface reflectance of KOMPSAT-3 satellite images and the land cover map of Environmental Systems Research Institute (ESRI). When the SAR image and the optical images are fused, the reliability of the soil moisture product can be improved. To validate the soil moisture mapping product, a comparative analysis was conducted with normalized difference water index (NDWI) products by the KOMPSAT-3 image and those of the Landsat-8 satellite. As a result, it was shown that the soil moisture map and NDWI of the study area were slightly negative correlated, whereas NDWI using the KOMPSAT-3 images and the Landsat-8 satellite showed a highly correlated trend. Finally, it will be possible to produce precise soil moisture using KOMPSAT optical images and KOMPSAT SAR images without other external remotely sensed images, if the soil moisture calculation algorithm used in this study is further developed for the KOMPSAT-5 image.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.10
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• pp.921-932
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• 2016

In this paper, a modified FSA(Frequency Scaling Algorithm) is proposed for KOMPSAT-5 high-resolution SAR image generation. In order to enhance performance of azimuth compression, degraded in sub-aperture processing due to the imperfect geometric parameter of data acquisition, the full-aperture signal processing algorithm is designed based on the exact time-frequency analysis. In addition, an azimuth scaling function is newly devised to make the full-aperture processing algorithm suitable for KOMPSAT-5 sliding-spotlight mode. Different from the previous sub-aperture FSA schemes, the suggested technique could accommodate the merit of unified signal processing structure regardless of operational modes of KOMPSAT-5. Through the point target simulation, it is verified that the suggested algorithm provides superior performance of azimuth compression over the existing full-aperture processing methods. The experimental results using real data acquired by KOMPSAT-5 are also given to demonstrate the effectiveness of our scheme as well.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.9
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• pp.723-732
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• 2017

Simplified factorizing-technique to improve the efficiency on computational procedure and the complexity of the conventional back-projection algorithm, which is used to reconstruct airborne FMCW-SAR image, is suggested, and the reconstruction process of SAR image by this simplified factorizing-technique are presented in this paper. This technique can be efficiently applied to airborne FMCW-SAR having a relatively narrow beamwidth and long synthetic aperture length, and its basic rationale is to exclude the data that has low level of contribution during computational procedure. Using the raw data of practical airborne FMCW-SAR system, performances of this proposed technique such as SAR image quality and processing time were compared and analyzed.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.171-184
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• 2023

Machine learning (ML)-based cavity detection using a large amount of survey data obtained from vehicle-mounted ground penetrating radar (GPR) has been actively studied to identify underground cavities. However, only simple image processing techniques have been used for preprocessing the ML input, and many conventional seismic and GPR data processing techniques, which have been used for decades, have not been fully exploited. In this study, based on the idea that a cavity can be identified using diffraction, we applied ML-based diffraction separation to GPR data to increase the accuracy of cavity detection using the YOLO v5 model. The original ML-based seismic diffraction separation technique was modified, and the separated diffraction image was used as the input to train the cavity detection model. The performance of the proposed method was verified using public GPR data released by the Seoul Metropolitan Government. Underground cavities and objects were more accurately detected using separated diffraction images. In the future, the proposed method can be useful in various fields in which GPR surveys are used.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.28-34
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• 2018

This paper is composed of a passenger management system using a temperature sensing module, a PIR sensor module for detecting movement inside a room, and a smart breath sensing module for determining a sleeping state. An embedded sensor module and a communication system integrated the sensing part and the algorithm driving part. As the aging society is accelerating and becoming more upgraded, the social cost of Silver Care increases, and in order to protect privacy, it is necessary to reduce costs by developing efficient smart silver care devices. The proposed non - image human body detection IOT sensor system is implemented by hardware and software and has superior performance compared with conventional image monitoring method.