• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.708-708
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• 2002

Recent developments in sensor technologies have provided remotely sensed data with very high spatial resolution. In order to fully utilize the potential of high resolution images, new image classification strategies are necessary. Unfortunately, the high resolution images increase the spectral within-field variability, and the classification accuracy of traditional methods based on pixel-based classification algorithms such as Maximum-Likelihood method may be decreased (Schiewe 2001). Recent development in Object Oriented Classification based on image segmentation algorithms can be used for the classification of forest patches on rugged terrain of Korea. The objectives of this paper are as follows. First, to compare the pros and cons of image classification methods based on pixel-based and object oriented classification algorithm for the forest patch classification. Landsat ETM＋ data and IKONOS data will be used for the classification. Second, to investigate ways to increase classification accuracy of forest patches. Supplemental data such as DTM and Forest Type Map of 1:25,000 scale are used for topographic correction and image segmentation. Third, to propose the best classification strategy for forest patch classification in terms of accuracy and data requirement. The research site for this paper is Namhansansung Provincial Park located at the eastern suburb of Seoul Metropolitan City for its diverse forest patch types and data availability. Both Landsat ETM＋ and IKONOS data are used for the classification. Preliminary results can be summarized as follows. First, topographic correction of reflectance is essential for the classification of forest patches on rugged terrain. Second, object oriented classification of IKONOS data enables higher classification accuracy compared to Landsat ETM＋ and pixel-based classification. Third, multi-stage segmentation is very useful to investigate landscape ecological aspect of forest communities of Korea.

• 대한원격탐사학회지
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• 제23권6호
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• pp.521-528
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• 2007

본 논문에서는 대상지역에 대한 영상을 다양한 가중치의 조합의 경우를 고려하여 객체 단위로 분할하게 되며 분할된 객체에 대하여 상호관계를 분석하여 수치적으로 표현하였다. 또한 최종적인 객체 기반영상분류에서 높은 정확도를 확보할 수 있는 가중치의 조합을 산정하였다. 연구에 사용된 영상은 Landsat-7/ETM 영상으로 대상 지역의 면적은 $11{\times}14$ Km이며 밴드 2, 3, 4의 조합을 사용하였다. 객체 간 계산은 Moran's I와 객체 내부 분산(Intrasegment Variance)을 이용하였다. 대상지역에 대하여 총 75개의 가중치 조합을 사용하여 75개의 객체 분할 영상을 생성하였다. 객체 분할 영상 중에 최종적인 영상 분류 시 높은 정확도가 예상되는 가중치 조합, 중간 정도 정확도가 예상되는 가중치 조합 그리고 낮은 정도 정확도가 예상되는 가중치 조합을 7개 선택하여 최종적인 객체기반 영상분류를 시행하고 그 정확도를 비교하였다. 정확도의 비교 결과, 가장 높은 정확도가 예상되는 가중치 조합의 객체 분할 영상의 경우 객체 기반 영상 분류 시 85% 이상의 정확도를 나타내었으며 반대로 낮은 경우는 분류 시 50% 정도의 분류 정확도를 나타내었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.297-300
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• 2006

The knowledge of fuel load and composition is important for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential of reduction the uncertainty in mapping fuels and offers the best approach for improving our abilities. This paper compared the results of object-oriented classification to a pixel-based classification for fuel type map derived from Hyperion hyperspectral data that could be enable to provide this information and allow a differentiation of material due to their typical spectra. Our methodological approach for fuel type map is characterized by the result of the spectral mixture analysis (SMA) that can used to model the spectral variability in multi- or hyperspectral images and to relate the results to the physical abundance of surface constitutes represented by the spectral endmembers. Object-oriented approach was based on segment based endmember selection, while pixel-based method used standard SMA. To validate and compare, we used true-color high resolution orthoimagery

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 춘계학술대회 논문집
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• pp.113-119
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• 2007

This article deals mainly with two objectives viz, 1) the potentiality of very high-resolution(VHR) multi-spectral and pan chromatic QuickBird satellite data in resources mapping over moderate resolution satellite data (IRS LISS III) and 2) the advantages of using object oriented classification method of eCognition software in land use and land cover analysis over the ISODATA classification method. These VHR data offers widely acceptable metric characteristics for cartographic updating and increase our ability to map land use in geometric detail and improve accuracy of local scale investigations. This study has been carried out in the Sukkalampatti mini-watershed, which is situated in the Eastern Ghats of Tamil Nadu, India. The eCognition object oriented classification method succeeded in most cases to achieve a high percentage of right land cover class assignment and it showed better results than the ISODATA pixel based one, as far as the discrimination of land cover classes and boundary depiction is concerned.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1221-1223
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• 2003

With speediness of economy, the structure of land use has taken lots of change. How can we quickly and exactly obtain detailed land use/cover change information, and then we know land resource amount, quality, distributing and change direction. More and more high resolution satellite systems are under development. So we can make good use of RS data, existed GIS data and GPS data to extract change information and update map. In this paper a fully automated approach for detecting land use/cover change using remote sensing data with object-oriented classification based on GIS data, GPS data is presented (referring to Fig.1). At same time, I realize integrating raster with vector methods of updating the basic land use/land cover map based on 3S technology and this is becoming one of the most important developing direction in 3S application fields; land-use and cover change fields over the world. It has been successful applied in two tasks of The Ministry of Land and Resources P.R.C and taken some of benefit.

• 환경영향평가
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• 제18권6호
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• pp.387-392
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• 2009

As Our society's environmental awareness and concern the significant increases, the importance of the legal system for environmental conservation such as the Prior Environmental Review System, Environmental Impact Assessment is growing increasingly. but, still critical issues are present such as reliability. Though there could be various causes such as the system or procedures etc. Above all, basically the environmental data problem is the critical cause. Therefore, this study was trying to improve the environmental data accuracy using the high-resolution color aerial photography, LiDAR data and Object Oriented Classification method. And in this study, classification based on coverage percentage of a particular species was attempted through the multi-resolution segmentation and multi-level classification method. The classification result was verified by comparison with 11 points local survey data. All 11 points were classified correctly. And even though the exact coverage percentage of the particular species did not be measured, It was confirmed that the species was occupied similar portion. It is important that the environmental data which can be used for the conservation value assessment could be acquired.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.426-429
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• 2006

Flood caused by Typhoon and severe rain during summer is the most destructive natural disasters in Korea. Almost every year flood has resulted in a big lost of national infrastructure and loss of civilian lives. It usually takes time and great efforts to estimate the flood-related damages. Government also has pursued proper standard and tool for using state-of-art technologies. High resolution satellite imagery is one of the most promising sources of ground truth information since it provides detailed and current ground information such as building, road, and bare ground. Once high resolution imagery is utilized, it can greatly reduce the amount of field work and cost for flood related damage assessment. The classification of high resolution image is pre-required step to be utilized for the damage assessment. The classified image combined with additional data such as DEM and DSM can help to estimate the flooded areas per each classified land use. This paper applied object-oriented classification scheme to interpret an image not based in a single pixel but in meaningful image objects and their mutual relations. When comparing it with other classification algorithms, object-oriented classification was very effective and accurate. In this paper, IKONOS image is used, but similar level of high resolution Korean KOMPSAT series can be investigated once they are available.

• Asia pacific journal of information systems
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• 제5권2호
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• pp.143-176
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• 1995

We present an overview of the Extensional Object Model (ExOM) and describe in detail the learning and classification components which integrate concepts from machine learning and object-oriented databases. The ExOM emphasizes flexibility in information acquisition, learning, and classification which are useful to support tasks such as diagnosis, planning, design, and database mining. As a vehicle to integrate machine learning and databases, the ExOM supports a broad range of learning and classification methods and integrates the learning and classification components with traditional database functions. To ensure the integrity of ExOM databases, a subsumption testing rule is developed that encompasses categories defined by type expressions as well as concept definitions generated by machine learning algorithms. A prototype of the learning and classification components of the ExOM is implemented in Smalltalk/V Windows.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.653-655
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• 2005

More than 70 percent of terrestrial territory of Korea is mountainous areas where degradation becomes serious year by year due to illegal tombs, expanding golf courses and stone mine development. We elaborate the potential usage of high resolution image for the monitoring of the phenomena. We made the classification of tombs and the statistical radiometric characteristics of graves were identified from this project. The graves could be classified to 4 groups from the field survey. As compared with grouping data after clustering and discriminant analysis, the two results coincided with each other. Object-oriented classification algorithm for feature extraction was theoretically researched in this project. And we did a pilot project, which was performed with mixed methods. That is, the conventional methods such as unsupervised and supervised classification were mixed up with the new method for feature extraction, object-oriented classification method. This methodology showed about $60\%$ classification accuracy for extracting tombs from satellite imagery. The extraction of tombs' geographical coordinates and graves themselves from satellite image was performed in this project. The stone mines and golf courses are extracted by NDVI and GVI. The accuracy of classification was around 89 percent. The location accuracy showed extraction of tombs from one-meter resolution image is cheaper and quicker way than GPS method. Finally we interviewed local government officers and made analyses on the current situation of mountainous area management and potential usage of KOMPSAT-II images. Based on the requirement analysis, we developed software, which is to management and monitoring system for mountainous area for local government.

• 한국정보처리학회논문지
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• 제6권9호
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• pp.2350-2364
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• 1999

본 논문은 객체지향 C++ 클래스 컴포넌트를 분류하여 재사용자에게 필요한 컴포넌트를 제공하기 위한 저장소의 클래스 라이브러리 설계방법을 제안한 것이다. 클래스 라이브러리를 설계하기 위해서 컴포넌트 구성 모델을 정의하였고, Enumerative 분류 방법을 이용한 멀티미디어 영역을 분류하였으며, 문서 클러스터링 방법을 확장하여 유사도에 의한 C++ 클래스를 유사한 그룹으로 분류하는 클러스터 생성 기준을 제안하고 있다. 이 유사 그룹인 클러스터는 클래스 멤버 데이터와 멤버함수 그리고 클래스 유사도를 기반으로 분류되며, 분류된 컴포넌트들은 유사도 관계의 계층구조로 구성된다. 마지막으로 객체지향 개념인 Generalization/Specialization의 C++ 상속관계를 계층구조로 표현할 수 있는 클래스 라이브러리를 설계하였다.