• Aerospace Engineering and Technology
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• v.3 no.1
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• pp.242-250
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• 2004

This paper describes the detection method of wire-like obstacles using millimeter-wave radar system. Passive sensor like CCD camera can be used for the detection of high power electric cables on the hills or mountains and it can give very good quality of obstacle target information. But this system is very limited to use by bad weather condition. The detection capability for different diameters of wire targets using millimeter radar system have been accomplished. To simulate the target on the moving helicopter, rotating targets are used with fixed radar system. In the experiment 11mm, 16mm and 22mm diameter of wires have been detected in single, two and three wires in one position. The detected signal from single wire was very clear on gray level image. Three wires placed very closely together could be recognized in range, cross range image plane. For two and three wires, blur effect due to mutual scattering effect is observed.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.12
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• pp.1388-1398
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• 2012

SAR(Synthetic Aperture Radar) systems can provide images of wide coverage in day, night, and all-weather conditions. However wideband SAR systems are known to be vulnerable to interferences from other devices operating at in-band or adjacent spectrums and this may lead to image corruptions. In this paper, a SAR point target simulator is developed that provides performance analysis on image distortion caused by interferences from other devices. Interference signals are generated based on the experimental data observed from acquired SAR raw data. Simulation results include typical SAR performance measures such as spatial resolution, peak to sidelobe ratio and integrated sidelobe ratio. Finally, SAR target simulations are performed and shown to correspond to the image corruptions found in real SAR missions affected by RF interferences.

• Proceedings of the International Union of Geodesy And Geophysics Korea Journal of Geophysical Research Conference
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• 2003.05a
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• pp.14-14
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• 2003

SAR (Synthetic Aperture Radar) is an imaging radar which can scan and image Earth System targets without solar illumination. Most Earth observation Shh systems operate in X-, C-, S-, L-, and P-band frequencies, where the shortest wavelength is approximately 1.5 cm. This means that most opaque objects in the SAR signal path become transparent and SAR systems can image the planetary surface targets without sunlight and through rain, snow and/or even volcanic ash clouds. Most conventional SAR systems in operation, including the Canada's RADARSAT-1, operate in one frequency and in one polarization. This has resulted in black and with images, with which we are familiar now. However, with the launching of ENVTSAT on March 1 2002, the ASAR system onboard the ENVISAT can image Earth's surface targets with selected polarimetric signals, HH+VV, HH+VH, and VV+HV. In 2004, Canadian Space Agency will launch RADARSAT-II, which is C-band, fully polarimetric HH+VV+VH+HV. Almost same time, the NASDA of Japan will launch ALOS (Advanced land Observation Satellite) which will carry L-band PALSAR system, which is again fully polarimetric. This means that we will have at least three fully polarimetric space-borne SAR system fur civilian operation in less than one year. Are we then ready for this new all weather Earth Observation technology\ulcorner Actual imaging process of a fully polarimetric SAR system is not easy to explain. But, most Earth system scientists, including geologists, are familiar with polarization microscopes and other polarization effects in nature. The spatial resolution of the new generation of SAR systems have also been steadily increased, almost to the limit of highest optical resolution. In this talk some new applications how they are used for Earth system observation purpose.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.37-37
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• 2003

Rice is one of the most important crop in the world and is a major export of Thailand. Optical sensors are not useful for rice monitoring, because most cultivated areas are often obscured by cloud during the growing period, especially in South East Asia. Spaceborne Synthetic Aperture Radar (SAR) such as RADARSAT, can see through regardless of weather condition which make it possible to monitor rice growth and to retrieve rice acreage, using the unique temporal signature of rice fields. This paper presents the result of a study of examining the backscatter behavior of rice using multi-temporal RADARSAT dataset. Ground measurements of paddy parameters and water and soil condition were collected. The ground truth information was also used to identify mature rice crops, orchard, road, residence, and aquaculture ponds. Land use class distributions from the RADARSAT image were analyzed. Comparison of the mean DB of each land use class indicated significant differences. Schematic representation of temporal backscatter of rice crop were plotted. Based on the study carried out in Pathum Thani Province test site, the results showed variation of sigma naught from first tillering vegatative phase until ripenning phase. It is suggested that at least, three radar data acquisitions taken at 3 stages of rice growth circle namely; those are at the beginning of rice growth when the field is still covered with water, in the ear differentiation period, and at the beginning of the harvest season, are required for rice monitoring. This pilot project was an experimental one aiming at future operational rice monitoring and potential yield predicttion.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.167-177
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• 2024

With advancements in satellite technology, interest in target detection and identification is increasing quantitatively and qualitatively. Synthetic Aperture Radar(SAR) images, which can be acquired regardless of weather conditions, have been applied to various areas combined with machine learning based detection algorithms. However, conventional studies primarily focused on the detection of stationary targets. In this study, we proposed a method to identify moving targets using an algorithm that integrates sub-aperture SAR images and cosine similarity calculations. Utilizing a transformer-based deep learning target detection model, we extracted the bounding box of each target, designated the area as a region of interest (ROI), estimated the similarity between sub-aperture SAR images, and determined movement based on a predefined similarity threshold. Through the proposed algorithm, the quantitative evaluation of target identification capability enhanced its accuracy compared to when training with the targets with two different classes. It signified the effectiveness of our approach in maintaining accuracy while reliably discerning whether a target is in motion.

• Korean Journal of Geomatics
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• v.3 no.1
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• pp.33-41
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• 2003

Every year, several typhoons hit the Korean peninsula and cause severe damage. For the prevention and accurate estimation of these damages, real time or almost real time flood information is essential. Because of weather conditions, images taken by optic sensors or LIDAR are sometimes not appropriate for an accurate estimation of water areas during typhoon. In this case SAR (Synthetic Aperture Radar) images which are independent of weather condition can be useful for the estimation of flood areas. To get detailed information about floods from satellite imagery, accurate classification of water areas is the most important step. A commonly- and widely-used classification methods is the ML(Maximum Likelihood) method which assumes that the distribution of brightness values of the images follows a Gaussian distribution. The distribution of brightness values of the SAR image, however, usually does not follow a Gaussian distribution. For this reason, in this study the ANN (Artificial Neural Networks) method independent of the statistical characteristics of images is applied to the SAR imagery. RADARS A TSAR images are primarily used for extraction of water areas, and DEM (Digital Elevation Model) is used as supplementary data to evaluate the ground undulation effect. Water areas are also extracted from KOMPSAT image achieved by optic sensors for comparison purpose. Both ANN and ML methods are applied to flat and mountainous areas to extract water areas. The estimated areas from satellite imagery are compared with those of manually extracted results. As a result, the ANN classifier performs better than the ML method when only the SAR image was used as input data, except for mountainous areas. When DEM was used as supplementary data for classification of SAR images, there was a 5.64% accuracy improvement for mountainous area, and a similar result of 0.24% accuracy improvement for flat areas using artificial neural networks.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.117-120
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• 2001

Synthetic aperture radar (SAR) is a promising active remote sensing technique to obtain large terrain information of the earth in all-weather conditions. SAR is useful in many applications, including terrain mapping and geographic information system (GIS), which use SAR display images. Usually, these applications need the enormous data storage because they deal with wide terrain images with high resolution. So, compression technique is a useful approach to deal with SAR display images with limited storage. Because there is some indispensable data loss through the conversion of a complex SAR image to a display image, some applications, which need high-resolution images, cannot tolerate more data loss during compression. Therefore, lossless compression is appropriate to these applications. In this paper, we propose a novel lossless compression technique for a SAR display image using one-step predictor and block arithmetic coding.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.75-86
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• 2012

Synthetic Aperture Radar(SAR) imaging system is independent of solar illumination and weather conditions; however, SAR image is difficult to interpret as compared with optical images. It has been increased interest in multi-sensor fusion technique which can improve the interpretability of $SAR^{\circ\circ}$ images by fusing the spectral information from multispectral(MS) image. In this paper, a multi-sensor fusion method based on high-frequency extraction process using Fast Fourier Transform(FFT) and outlier elimination process is proposed, which maintain the spectral content of the original MS image while retaining the spatial detail of the high-resolution SAR image. We used TerraSAR-X which is constructed on the same X-band SAR system as KOMPSAT-5 and KOMPSAT-2 MS image as the test data set to evaluate the proposed method. In order to evaluate the efficiency of the proposed method, the fusion result was compared visually and quantitatively with the result obtained using existing fusion algorithms. The evaluation results showed that the proposed image fusion method achieved successful results in the fusion of SAR and MS image compared with the existing fusion algorithms.

• Journal of the Korean Society for Marine Environment & Energy
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• v.16 no.1
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• pp.1-8
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• 2013

This study focuses on a ship detection and tracking method using Frequency Modulated Continuous Wave (FMCW) radar used for horizontal surveillance. In general, FMCW radar can play an important role in maritime surveillance, because it has many advantages such as low warm-up time, low power consumption, and its all weather performance. In this paper, we introduce an effective method for data and signal processing of ship's detecting and tracking using the X-band radar. Ships information was extracted using an image-based processing method such as the land masking and morphological filtering with a threshold for a cycle data merged from raw data (spoke data). After that, ships was tracked using search-window that is ship's expected rectangle area in the next frame considering expected maximum speed (19 kts) and interval time (5 sec). By using this method, the tracking results for most of the moving object tracking was successful and those results were compared with AIS (Automatic Identification System) for ships position. Therefore, it can be said that the practical application of this detection and tracking method using FMCW radar improve the maritime safety as well as expand the surveillance coverage cost-effectively. Algorithm improvements are required for an enhancement of small ship detection and tracking technique in the future.

• Journal of the Korea Institute of Military Science and Technology
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• v.5 no.3
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• pp.62-76
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• 2002

This paper describes the design of a Coordinates Tracking algorithm for EOTS and its error analysis. EOTS stabilizes the image sensors such as FLIR, CCD TV camera, LRF/LD, and so on, tracks targets automatically, and provides navigation capability for vehicles. The Coordinates Tracking algorithm calculates the azimuth and the elevation angle of EOTS using the inertial navigation system and the attitude sensors of the vehicle, so that LOS designates the target coordinates which is generated by a Radar or an operator. In the error analysis in this paper, the unexpected behaviors of EOTS that is due to the time delay and deadbeat of the digital signals of the vehicle equipments are anticipated and the countermeasures are suggested. This algorithm is verified and the error analysis is confirmed through simulations. The application of this algorithm to EOTS will improve the operational capability by reducing the time which is required to find the target and support especially the flight in a night time flight and the poor weather condition.