• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.527-536
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• 2010

This paper reports a reliable GRD (Ground Resolved Distance) measurement method of using natural targets instead of the method using artificial targets. For this, we developed an edge profile extraction technique suitable for natural targets. We demonstrated the accuracy and stability of this technique firstly by comparing GRD values generated by this technique visually inspected GRD values for artificial targets taken in laboratory environments. We then demonstrated the feasibility of GRD estimation from natural targets by comparing GRD values from natural targets to those from artificial targets using satellite images containing both artificial and natural targets. The GRDs measured from the proposed method were similar to the values from visual inspection and the GRDs measured from the natural targets were similar to the values from artificial targets. These results support our proposed method is able to measure reliable GRD from natural targets.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.587-599
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• 2011

As one measure of image interpretability, NIIRS(National Imagery Interpretability Rating Scale) has been used. Unlike MTF(Modulation Transfer Function), SNR(Signal to Noise Ratio), and GSD(Ground Sampling Distance), NIIRS can describe the quality of overall image at user's perspective. NIIRS is observed with human observation directly or estimated by edge analysis. For edge analysis specially manufactured artificial target is used commonly. This target, formed with a tarp of black and white patterns, is deployed on the ground and imaged by the satellite. Due to this, the artificial target-based method needs a big expense and can not be performed often. In this paper, we propose a new edge analysis method that enables to estimate NIIRS accurately. In this method, natural targets available in the image are used and characteristics of the target are considered. For assessment of the algorithm, various experiments were carried out. The results showed that our algorithm can be used as an alternative to the artificial target-based method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.305-313
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• 2021

The upcoming warfare will be network-centric warfare with the acquiring and sharing of information on the battlefield through the connection of the entire weapon system. Therefore, the amount of information generated increases, but the technology of evaluating the information is insufficient. Threat assessment is a technology that supports a quick decision, but the information has many uncertainties and is difficult to apply to an advanced battlefield. This paper proposes a threat assessment based on artificial intelligence while removing the target uncertainty. The artificial intelligence system used was a fuzzy inference system and a multi-layer perceptron. The target was classified by inputting the unique characteristics of the target into the fuzzy inference system, and the classified target information was input into the multi-layer perceptron to calculate the appropriate threat value. The validity of the proposed technique was verified with the threat value calculated by inputting the uncertain target to the trained artificial neural network.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.1-8
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• 2023

Modeling of artificial targets in Synthetic Aperture radar (SAR) mainly simulates radar signals reflected from the faces and edges of the 3D Computer Aided Design (CAD) model with a ray-tracing method, and modeling of the clutter on the Earth's surface uses a method of distinguishing types with similar distribution characteristics through statistical analysis of the SAR image itself. In this paper, man-made targets on the surface and background clutter on the terrain are integrated and made into a three-dimensional (3D) point cloud scatterer model, and SAR image were created through computational signal processing. The results of the SAR Stripmap image generation of the actual automobile based SAR radar system and the results analyzed using EM modeling or statistical distribution models are compared with this 3D point cloud scatterer model. The modeling target is selected as an bridge because it has the characteristic of having both water surface and ground terrain around the bridge and is also a target of great interest in both military and civilian use.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.433-440
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• 2015

In stochastic hyperspectral target detection algorithms, the target signal components may be included in the background characterization if targets are not rare in the image, causing target leakage. In this paper, the effect of target leakage is analysed and an improved hyperspectral target detection method is proposed by excluding the pixels which have similar reflectance spectrum with the target in the process of background characterization. Experimental results using the AISA airborne hyperspectral data and simulated data with artificial targets show that the proposed method can dramatically improve the target detection performance of matched filter and adaptive cosine estimator. More studies on the various metrics for measuring spectral similarity and adaptive method to decide the appropriate amount of exclusion are expected to increase the performance and usability of this method.

• The Journal of the Acoustical Society of Korea
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• v.34 no.6
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• pp.487-492
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• 2015

A short-range underwater target detection and identification techniques using mid- and high-frequency bands have been highly developed. However, nowadays the long-range detection using the low-frequency band is requested and one of the most challengeable issues. The waveform inversion technique is widely used and the hottest technology in both academia and industry of the seismic exploration. It is based on the numerical analysis tool, and could construct more than a few kilometers of the subsurface structures and model-parameters such as P-wave velocity using a low-frequency band. By applying this technique to the underwater acoustic circumstance, firstly application of underwater target detection is verified. Furthermore, subsurface structures and it's parameters of the war-field are well reconstructed. We can confirm that this technique greatly reduces the false-alarm rate for the underwater targets because it could accurately reproduce both the shape and the model-parameters at the same time.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.147-150
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• 2000

본 논문은 무인감시 시스템의 특성상 조명 상태의 변화나 카메라의 흔들림과 같은 환경의 변화에 적응할 수 있도록 연속된 세 프레임간의 차영상를 이용하는 방법을 적용하여 움직임 정보를 추출하고, 영역의 분할 및 특징점 추출을 수행한 후에, 인공 신경회로망 기법을 적용하여 이동표적을 추적한다. 추적시에는 추출된 각각의 표적간의 데이터 연결을 움직임 정보의 특징점들을 이용, 레이블링하여 각각의 표적을 연결시켜 추적의 성능을 높였다.

• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.99-107
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• 2019

Until now it has had the limitation of the target in the US JMEM to calculate the Pk with the existing method by our study. In this study, we focused on deriving a method to calculate the Pk of the actual targets except JMEM targets using ANN. We study the initial predictive model of ANN(Artificial Neural Network) from the targets data of the specification and the vulnerable area in the US JMEM(Joint Munitions Effectiveness Manuals), and calculate the actual targets vulnerable area by using this method. Finally, we propose a method to calculate the Pk by applying those data to the existing method of us.

• ICROS
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• v.3 no.5
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• pp.31-37
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• 1997

본 연구로부터 최종적으로 얻을 수 있는 성과는 비행중 표적 포착과 인식을 위한 실시간 표적 인식 및 추적 기법에 대한 기반 기술과 차세대 호밍 유도탄 개발을 위한 기반 기술 확보라 할 수 있다. 단계별로는 제 1단계에서 2차원 인식/추적 기법과 이의 실시간 구현을 위한 기초 소프트웨어 및 하드웨어에 관한 연구결과를 기반으로 하여, 2단계에서는 가리워짐이 있는 상황에서의 2차원 인식, 3차원 모델에 기반한 인식 및 추적, 센서 퓨전, 그리고 3단계에서는 인식과 추적의 통합, 인공지능의 기초 기술에 관한 결과를 얻을 수 있다.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.135-140
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• 2009

In this paper, we synthesized active sonar target signals based on highlights model, and then carried out target classification using the synthesized signals. If the target aspect angle is changed, the different signals are synthesized. To know the result, two different experiments are done. First, The classification results with respect to each aspect angle are shown. Second, the results in two group in aspect angle are acquired. Time domain feature extraction is done using matched filter and envelope detection. It shows the pattern of each highlights. Artificial neural networks and multi-class SVM are used for classifying target signals.