• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.534-537
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• 2005

Polarization of the radar wave refers to the ellipticity angle and orientation angle of the polarization ellipse. An evaluation of the polarization response can help understand the scattering mechanisms involved for a particular area of interest or provide information for image classification and algorithm section. C- and L-band polarization responses measured at urban area show the results that the polarization behavior for dihedral comer reflector or short, thin cylinder reflector appears at located in city streets or buildings site which are lined up and the polarization behavior for a large conducting sphere appears at parts of test site particularly river, flat, and vegetated areas. Also, the co- and cross-polarized response graphs and polarimetric parameters are discussed.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.550-553
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• 2005

To acquire radar target signature, various kinds of target are necessary. Measurement is one of the data acquiring method, but much time and high cost is required to get the target data from the real targets. Even if we can afford that, the targets we can access are very limited. To obtain target signatures avoiding these problems, we build the target CAD (Computer Aided Design) model for the calculation of target signatures. To speed up RCS calculation, we applied adaptive super-sampling and tested quite complex tank CAD model which is 1.4 hundred of thousands facet. We use calculated RCS data for ID range profile and 2D ISAR (Inverse Synthetic Aperture Radar) image formation. We adopted IFFT (Inverse Fast Fourier Transform) algorithm combined with polar formatting algorithm for the ISAR imaging. We could confirm the possibility of the construction of database from the images of CAD models for target classification applications.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.321-339
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• 2017

In this work, we are interested in the extraction of areas of interest from satellite images by introducing a MO-TRIBES/OC-SVM approach. The One-Class Support Vector Machine (OC-SVM) is based on the estimation of a support that includes training data. It identifies areas of interest without including other classes from the scene. We propose generating optimal training data using the Multi-Objective TRIBES (MO-TRIBES) to improve the performances of the OC-SVM. The MO-TRIBES is a parameter-free optimization technique that manages the search space in tribes composed of agents. It makes different behavioral and structural adaptations to minimize the false positive and false negative rates of the OC-SVM. We have applied our proposed approach for the extraction of earthquakes and urban areas. The experimental results and comparisons with different state-of-the-art classifiers confirm the efficiency and the robustness of the proposed approach.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.618-620
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• 2004

This paper explores the use of IKONOS imagery of 1 m resolution panchromatic (PAN) band and 4 m resolution multi-spectral (MS) band in the development of agriculture­related data inventories. Three images (May 25, 2001, December 25, 2001, October 23, 2003) were used to obtain temporal distributions in crop cover characteristics such as rice, pear, grape, red pepper, corn, barley, garlic and surface water cover of reservoir with field investigations. The availability and cost problems are expected to solve by KOMPSAT-2 that is scheduled to launch in 2005. The capability of KOMPSAT-2 image for crop and rural water resources management will increase by accumulating temporal data inventories as a database.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.206-209
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• 2006

강우-유출 모델링에 있어 토지피복/이용 상태는 중요한 입력변수로 사용되지만 기존의 다중분광영상을 이용한 분류에는 한계가 있다. 본 연구에서는 위성탑재 초분광영상인 Hyperion 영상의 분광혼합분석을 통해 도시지역의 수문학적 토지피복/이용 분류를 실시하였으며 분류등급의 기준은 널리 사용되고 있는 SCS 토지피복/이용 등급을 이용하였다. 정확도분석을 위해 항공사진을 디지타이징하여 불투수면적의 비율을 비교하였으며 분광혼합분석 결과와 항공사진에서 불투수면적의 비율은 유사하게 나타났다. 그러나 SCS의 분류등급은 미국을 기준으로 개발되었기 때문에 임계치를 이용하여 분류된 등급과 실제 항공사진판독의 결과가 일부 다르게 나타나는 것을 알 수 있었다.

• Proceedings of the KSRS Conference
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• v.1
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• pp.188-190
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• 2006

A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The local segmentor of the first stage performs regiongrowing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. This stage uses a sliding window strategy with boundary blocking to alleviate a computational problem in computer memory for an enormous data. The global segmentor of the second stage has not spatial constraints for merging to classify the segments resulting from the previous stage. The experimental results show that the new approach proposed in this study efficiently performs the segmentation for the images of very large size and an extensive number of bands

• The Journal of the Acoustical Society of Korea
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• v.15 no.1E
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• pp.29-33
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• 1996

A categorization for cluster is necessary when an unsupervised classfication is used for remote sensing image classification. It is desirable that this method is performed automatically, because manual categorization is a highly time consuming process. In this paper, several automatic determination methods were proposed and evaluated. They are four methods. a) maximum number method : which assigns the tharget cluster to the category which occupies the largest area of that cluster b) maximum percentage method : which assigns the target cluster to the category which shows the maximum percentage within the category in that cluster. c) minmun distance method : which assigns the target cluster to the category having minmum distance with that cluster d) element ratio matching method : which assigns local regions to the category having the most similar element ratio of that region From the results of the experiments, it was certified that the result of minimum distance method was almost the same as the result made by a human operator.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.207-212
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• 2009

고해상도 위성영상이 갖는 공간 객체의 복잡성과 다양성에 의해 기존 중 저해상도 영상에서 사용하던 분류 방식을 고해상도 영상에 그대로 적용하기에는 한계가 있다. 이러한 문제를 극복하기 위하여 영상은 공간적인 특성을 추가적으로 추출하여 분광정보와 결합하여 분류를 수행하는 방식의 연구가 진행되고 있다. 본 연구의 목적은 고해상도 영상의 분류정확도를 개선하기 위하여 새로운 공간 개체(spatial feature)인 SSI(Shape-Size Index)를 제안하는데 있다. SSI는 영역 확장(Region Growing) 기반의 영상 분할(Image Segmentation)을 수행한 후, 객체 내에 객체의 크기와 모양에 대한 고려를 모두 할 수 있는 공간 속성값을 할당하여 공간정보를 추출한다. 추출된 공간정보를 고해강도 영상의 다중분광 밴드와 결합하여 Support Vector Machine(SVM)을 이용한 분류를 수행하였다. 실험 결과, 제안한 기법의 분류 결과가 분광밴드만을 이용하여 분류를 수행한 결과뿐만 아니라 기존의 공간 개체 추출방식인 GLCM, PSI 기법을 이용한 분류 결과에 비해 높은 분류정확도를 도출함을 알 수 있었다.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.224-230
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• 2008

본 연구는 산림 내 임상을 구획하기 위해 고해상도 IKONOS 위성영상을 객체 지향기반으로 분할 및 분류하였다. 영상분할 시 분광정보와 공간정보를 동시에 이용하여 모양이나 분광정보에 있어서 동질한 영역이라고 정의되는 영상객체를 생성하였다. 분할된 영상을 분류계급(class)으로 분류하기 위하여 NDVI와 경사, 방위, 고도 등 지형인자를 새로운 레이어로 추가시키고, 분류개념을 형성하기 위하여 퍼지 규칙을 사용하였다. 영상의 획득시기가 5월초인 점을 감안하여 NDVI는 0.2, 경사 $^{\circ}5^{\circ}$ 그리고 고도 130m를 기준으로 산림과 비산림지역을 분류할 수 있었고, 지형인자에 영향을 많이 받는 굴참나무와 신갈나무 또한 효율적으로 분류할 수 있었다.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.103-109
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• 1998

A technique on atmospheric correction algorithm for a single band (0.76-0.90 $\mu$m) reflective of Landsat TM imagery has been developed using a radiation transfer model simulation. It proceeds in two steps: First, calculation of the surface reflectance of each pixel based on precomputed planetary albedo functions for actual atmospheres(e. g. radiosonde) and two kinds of atmospheric visibility states. Second, approximate correction of the adjacency pixel effect by taking into account the average reflectance in an 7 $\times$ 7 pixel neighbourhood and using appropriate land cover classification in reflectance. The correction functions are provided by MODTRAN model.