• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.11
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• pp.1063-1069
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• 2011

NCTR(Non-Cooperative Target Recognition) and ATR(Automatic Target Recognition) are methodologies to identify military targets using radar, optical, and infrared images. Among them, a strategy to recognize ground targets using synthetic aperature radar(SAR) images is called SAR ATR. In general, SAR ATR consists of three sequential stages: detection, discrimination and classification. In this paper, a modification of the polar mapping classifier(PMC) to identify inverse SAR(ISAR) images has been made in order to apply it to SAR ATR. In addition, a preprocessing scheme can mitigate the effect from the clutter, and information on the shadow is employed to improve the classification accuracy.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.801-810
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• 2018

Ship monitoring using satellite synthetic aperture radar (SAR) images consists of ship detection, ship discrimination, and ship classification. A large number of methods have been proposed to improve the detection and discrimination capabilities, while only a few studies exist for ship classification. Thus, many studies for the ship classification are needed to construct ship monitoring system having high performance. Note that constructing database (DB), which contains both SAR images and labels of various ships, is important for research on the ship classification. In the airborne SAR classification, many methods have been developed using moving and stationary target acquisition and recognition (MSTAR) DB. However, there has been no publicly available DB for research on the ship classification using satellite SAR images. Recently, Shanghai Key Laboratory has constructed OpenSARShip DB using both SAR images of various ships generated from Sentinel-1 satellite of European Space Agency (ESA) and automatic identification system (AIS) information. Thus, the applicability of OpenSARShip DB for ship classification should be investigated by using the concepts of airborne SAR classification which have shown high performances. In this study, ship classification using satellite SAR images are conducted by applying the concepts of airborne SAR classification to OpenSARShip DB, and then the applicability of OpenSARShip DB is investigated by analyzing the classification performances.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.8
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• pp.1002-1009
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• 2012

This paper shows the research about radar target recognition using the measured radar signals from MSU(Michgan State University) bistatic radar system. In this research, we first did the bistatic measurements at $30^{\circ}$, $60^{\circ}$, $90^{\circ}$ using F-14, Mig-29, and F-22 scale models. Then, we extract the target feature vectors using time-frequency analysis methods such as STFT(Short Time Fourier Transform) and CWT(Continous Wavelet Transform) and perform the target classification test using MLP(Multi-layerd Perceptron) neural network. The results show that the target classification performance is too much dependent on the bistatic angles and the best performance is obtained at the $60^{\circ}$ bistatic angle.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.33-39
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• 2017

In this paper, an effective feature which is capable of classifying targets among the detections obtained from 2D range-bearing maps generated in active sonar environments is proposed. Most conventional approaches for target classification with the 2D maps have considered magnitude of peak and statistical features of the area surrounding the peak. To improve the classification performance, HOG(Histogram of Gradient) feature, which is popular for their robustness in the image textures analysis is applied. In order to classify the target signal, SVM(Support Vector Machine) method with reduced HOG feature by the PCA(Principal Component Analysis) algorithm is incorporated. The various simulations are conducted with the real clutter signal data and the synthesized target signal data. According to the simulated results, the proposed method considering HOG feature is claimed to be effective when classifying the active sonar target compared to the conventional methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.125-130
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• 2018

Linear discriminant analysis is a statistical analysis method that is generally used for dimensionality reduction of the feature vectors or for class classification. However, in the case of a data set that cannot be linearly separated, it is possible to make a linear separation by mapping a feature vector into a higher dimensional space using a nonlinear function. This method is called generalized discriminant analysis or kernel discriminant analysis. In this paper, we carried out target classification experiments with active sonar target signals available on the Internet using both liner discriminant and generalized discriminant analysis methods. Experimental results are analyzed and compared with discussions. For 104 test data, LDA method has shown correct recognition rate of 73.08%, however, GDA method achieved 95.19% that is also better than the conventional MLP or kernel-based SVM.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.116-122
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• 2010

This paper proposes a method for classifying targets robust to geometric transformations of targets such as rotation, scale change, translation, and pose change. Targets which have rotation, scale change, and shift is firstly classified based on CM(Confidence Map) which is generated by similarity, scale ratio, and range of orientation for SIFT(Scale-Invariant Feature Transform) feature vectors. On the other hand, DB(DataBase) which is acquired in various angles is used to deal with pose variation of targets. Range of the angle is determined by comparing and analyzing the execution time and performance for sampling intervals. We experiment on various images which is geometrically changed to evaluate performance of proposed target classification method. Experimental results show that the proposed algorithm has a good classification performance.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.389-392
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• 2004

Classification of Earth natural components within a full polarimetric SAR image is one of the most important applications of radar polarimetry in remote sensing. In this paper, the unsupervised classification algorithms based on the combined use of the polarimetric processing technique such as the target decomposition and statistical complex Wishart classification method are evaluated and applied to vegetated terrain in Jeju volcanic island.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2003.11a
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• pp.193-197
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• 2003

Radar target identification can be achieved by using various radar signatures, such as one-dimensional(1-D) range profile, 2-D radar images, and 1-D or 2-D scattering centers on a target. In this letter, five 1-D scattering center extraction methods are discussed - TLS(Total Least Square)-Prony, Fast Root-MUSIC (Multiple Signal Classification), Matrix-Pencil, GEESE(GEneralized Eigenvalues utilizing Signal-subspace Eigenvalues), TLS-ESPRIT(Total Least Squares - Estimation of Signal Parameters via Rotational Invariance Technique), These methods are compared in the context of estimation accuracy as well as a computational efficiency using a noisy data. Finally these methods are applied to the target classification experiment with the measured data in the POSTECH compact range facility.

• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.57-67
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• 2015

The concept of detection and classification of objects based on infrared camera is widely applied to military applications. While the object detection technology using infrared images has long been researched and the latest one can detect the object in sub-pixel, the object classification technology still needs more research. In this paper, we present object classification method based on measured radiant intensity of objects such as target, artillery, and missile using infrared camera. The suggested classification method was verified by radiant intensity measuring experiment using black body. Also, possible measuring errors were compensated by modelling-based correction for accurate radiant intensity measure. After measuring radiation of object, the model of radiant intensity is standardized based on theoretical background. Based on this research, the standardized model can be applied to the object classification by comparing with the actual measured radiant intensity of target, artillery, and missile.

• Journal of Korean Society of Rural Planning
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• v.18 no.3
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• pp.111-122
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• 2012

This study is to evaluate the classification system of rural-villages remodeling components which is provided for improving the quality of life for rural community by improvement of settlement environment. To achieve this, rural-villages remodeling components are classified according to the spatial structure of rural area through analysis of literature, then we have examined the applicability through case studies after modification work which is based on experts's discussion and rearrangement by pilot investigation of researcher. In the classification system of rural-villages remodeling classified productivity area, residential area, community area in first group and this classification is divided into 6 space to production, 4 space to residence, 5 space to community in second group by literature search, pilot investigation of researcher and field survey. The subject elements surveyed a total of 123 through the literature search, additionally, 1 element at a space to production and space to community in field survey for types in zoning cases. As a result, categories and items are decided that it is included 125 target elements.