• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.3
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• pp.311-320
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• 1999

This study conducts to analyze comparative accuracy when both Image Transformation Methods and Supervised Classifications on multi-spectral TM using a case of Lower Tumen River Area. In terms of overall classification accuracy, maximum likelihood method turns out higher than other one, but in a case of vegetation only, MNF and TC image transformation methods produce a better quality of the result. Especially, seven dimensional images including MNF, TC, and NDVI create better image than three dimensional one. Among these transformation methods, maximum likelihood method results out the best one. Multi-spectral image could be useful as an important basic material for site selection of industrial allocation as well as Tumen River Area Economic Development Plan.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.686-697
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• 2018

Due to the advance in remote sensing technology, it has become easier to more frequently obtain high resolution imagery to detect delicate changes in an extensive area, particularly including forest which is not readily sub-classified. Time-series analysis on high resolution images requires to collect extensive amount of ground truth data. In this study, the potential of land coverage mapas ground truth data was tested in classifying high-resolution imagery. The study site was Wonju-si at Gangwon-do, South Korea, having a mix of urban and natural areas. KOMPSAT-3A imagery taken on March 2015 and land coverage map published in 2017 were used as source data. Two pixel-based classification algorithms, Support Vector Machine (SVM) and Random Forest (RF), were selected for the analysis. Forest only classification was compared with that of the whole study area except wetland. Confusion matrixes from the classification presented that overall accuracies for both the targets were higher in RF algorithm than in SVM. While the overall accuracy in the forest only analysis by RF algorithm was higher by 18.3% than SVM, in the case of the whole region analysis, the difference was relatively smaller by 5.5%. For the SVM algorithm, adding the Majority analysis process indicated a marginal improvement of about 1% than the normal SVM analysis. It was found that the RF algorithm was more effective to identify the broad-leaved forest within the forest, but for the other classes the SVM algorithm was more effective. As the two pixel-based classification algorithms were tested here, it is expected that future classification will improve the overall accuracy and the reliability by introducing a time-series analysis and an object-based algorithm. It is considered that this approach will contribute to improving a large-scale land planning by providing an effective land classification method on higher spatial and temporal scales.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.235-244
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• 2016

In large-area crop classification with MODIS data, a mixed pixel problem caused by the low resolution of MODIS data has been one of main issues. To mitigate this problem, this paper proposes a hierarchical classification algorithm that selectively classifies the specific crop class of interest by using their spectral characteristics. This selective classification algorithm can reduce mixed pixel effects between crops and improve classification performance. The methodological developments are illustrated via a case study in Jilin city, China with MODIS Normalized Difference Vegetation Index (NDVI) and Near InfRared (NIR) reflectance datasets. First, paddy fields were extracted from unsupervised classification of NIR reflectance. Non-paddy areas were then classified into corn and bean using time-series NDVI datasets. In the case study result, the proposed classification algorithm showed the best classification performance by selectively classifying crops having similar spectral characteristics, compared with traditional direct supervised classification of time-series NDVI and NIR datasets. Thus, it is expected that the proposed selective hierarchical classification algorithm would be effectively used for producing reliable crop maps.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.187-195
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• 2013

Most of former studies for car license plate detection restrict the image acquisition environment. The aim of this research is to diminish the restrictions by proposing a new method of using SIFT and neural network. SIFT can be used in diverse situations with less restriction because it provides size- and rotation-invariance and large discriminating power. SIFT extracted from the license plate image is divided into the internal(inside class) and the external(outside class) ones and the classifier is trained using them. In the proposed method, by just putting the various types of license plates, the trained neural network classifier can process all of the types. Although the classification performance is not high, the inside class appears densely over the plate region and sparsely over the non-plate regions. These characteristics create a local feature map, from which we can identify the location with the global maximum value as a candidate of license plate region. We collected image database with much less restriction than the conventional researches. The experiment and evaluation were done using this database. In terms of classification accuracy of SIFT keypoints, the correct recognition rate was 97.1%. The precision rate was 62.0% and recall rate was 50.2%. In terms of license plate detection rate, the correct recognition rate was 98.6%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.2
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• pp.11-20
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• 2010

In this paper we propose a face detection system which consists of a method of face candidate extraction using skin color and a method of face verification using the feature of facial structure. Firstly, the proposed extraction method of face candidate uses the image segmentation and merging algorithm in the regions of skin color and the neighboring regions of skin color. These two algorithms make it possible to select the face candidates from the variety of faces in the image with complicated backgrounds. Secondly, by using the partial face classifier, the proposed face validation method verifies the feature of face structure and then classifies face and non-face. This classifier uses face images only in the learning process and does not consider non-face images in order to use less number of training images. In the experimental, the proposed method of face candidate extraction can find more 9.55% faces on average as face candidates than other methods. Also in the experiment of face and non-face classification, the proposed face validation method obtains the face classification rate on the average 4.97% higher than other face/non-face classifiers when the non-face classification rate is about 99%.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.2 s.8
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• pp.137-147
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• 1996

In this paper, the performances between maximum likelihood classifier based on statistical classification and multilayer perceptrons based on neural network approaches were compared and evaluated Experimental results from both neural network method and statistical method are presented. In addition, the nature of two different approches are analyzed based on the experiments.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.253-255
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• 2016

본 논문에서는 다중 지역 이진 패턴(Multi-scale Bock LBP, MB-LBP) 특징과 랜덤 포레스트에 기반한 새로운 기법의 머리 방향 분류 기법을 제안한다. 제안 기법에서는 occlusion 과 조명의 변화에 강인한 분류 정확도를 얻기 위해서 랜덤화된 트리를 학습하는 것을 목표로 한다. 우선, 얼굴 이미지로부터 많은 MB-LBP 특징을 추출하고, 얼굴 영상들을 랜덤하게 입력하고 MB-LBP 크기 파라미터와 같은 랜덤 특징과 블록 좌표들을 사용하여 트리를 생성한다. 게다가 각 노드에서 정보 이득을 최대화 하는 트리의 내부 노드를 생성하기 위해서 uniform LBP 의 특성을 고려한 분할 함수를 개발한다. 랜덤화된 트리는 랜덤 포레스트에 포함되어 있으며 마지막 결정단계에서 Maximum-A-Posteriori criterion 으로 최종 결정을 한다. 실험 결과는 제안 기법이 다양한 조명, 자세, 표현, occlusion 상황에서 기존의 방법보다 개선된 성능으로 머리 방향을 분류 할 수 있음을 보여준다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 1999.12a
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• pp.16-20
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• 1999

우리나라 특히 서ㆍ남해안은 전형적인 리아스식 해안으로 해안선 출입이 잦아 해안의 대표적인 지형들이 골고루 발달해 있는 곳이며, 해안생물자원도 풍부한 지역이다. 종래의 생태계 조사방법은 종목록을 작성하는 수준이었기 때문에 자연자원과 생태계 보존 및 관리에 필요한 해안생태계의 서식지 유형을 분류하여 지도화하는 작업이 우선적으로 이루어져야 한다. 이 연구는 태안해안국립공원 지역을 대상으로 다음의 세가지 연구를 시행하였다. 첫째, 해안생물의 서식 기반이 되는 물리적 요소인 기질, 경사도, 파랑에너지에 입각하여 서식지 유형을 분류하였다 둘째, 해안의 생태계 보존 및 관리에 영향을 미치는 토지이용 현황을 원격탐사를 이용하여 분류하였다. 셋째, 서식지와 토지이용을 결합하여 암석해안과 갯벌(모래, 펄)의 생태계 단위를 설정하였다. 이러한 생태계 단위는 생태적으로 관리전략을 세울 수 있는 토대가 될 수 있다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.146-154
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• 2014

The local binary pattern (LBP) scheme that is one of the texture classification methods normally uses the distribution of flat, edge and corner patterns. However, it cannot examine the edge direction and the pixel difference because it is a sort of binary pattern caused by thresholding. Furthermore, since it cannot consider the pixel distribution, it shows lower performance as the image size becomes larger. In order to solve this problem, we propose a sub-classification method using the edge direction distribution and eigen-matrix. The proposed sub-classification is applied to the particular texture patches which cannot be classified by LBP. First, we quantize the edge direction and compute its distribution. Second, we calculate the distribution of the largest value among eigenvalues derived from structure matrix. Simulation results show that the proposed method provides a higher classification performance of about 8 % than the existing method.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.263-263
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• 2022

물봉선속(Impatiens L.)은 일년생 또는 다년생 초본으로 우리나라에 5-7종이 분포한다. 물봉선의 화장품용 항산화제 및 천연 방부제로서의 유용성이 밝혀졌으며, 최근 항염, 항산화 등 생리활성에 대한 연구가 국내 자생하는 물봉선 및 노랑물봉선을 대상으로 수행된 바 있으나 이하 분류군에 대한 연구는 미비한 실정이다. 본 연구에서는 국내 자생하는 물봉선속 분류군들의 항산화활성과 생리 활성물질의 함량을 분석하고 지역 간 분류군별 차이를 확인하고자 하였다. 따라서 국내 자생하는 물봉선속 분류군들의 항산화활성을 검정하기 위하여 DPPH, ABTS, TPC, TFC 4가지 방법을 이용하여 분석하였다. 국내 자생하는 물봉선속 5분류군 가야물봉선(Impatiens atrosanguinea (Nakai) B.U.Oh & Y.P.Hong), 노랑물봉선(Impatiens noli-tangere L), 물봉선(Impatiens textorii Miq), 미색물봉선(Impatiens noli-tangere var. pallescens Nakai), 처진물봉선(Impatiens furcillata Hemsl)이 12개 지역에서 수집되었으며, 잎 추출물(70% 에탄올)에 대해 분석되었다. 물봉선속 분류군들의 잎 추출물의 DPPH 라디칼 소거 활성 검정 결과 가야물봉선(4.91 ± 3.00 mgAAE/g)이 가장 높았고 처진물봉선(1.77 ± 0.55 mgAAE/g)이 가장 낮았으며, ABTS의 경우 가야물봉선(3.14 ± 1.35 mgAAE/g)로 가장 높았고 미색물봉선(1.87 ± 0.16 mgAAE/g)이 가장 낮았다. TPC의 경우 미색물봉선(5.48 ± 1.05 ugGAE)이 가장 높았고 노랑물봉선(2.78 ± 1.98 ugGAE)이 가장 낮았으며, TFC의 경우 물봉선(0.70 ± 0.20 ugGAE/g)이 가장 높았고 노랑물봉선(0.45 ± 0.08 ugGAE/g)이 가장 낮게 나타났다. 수집지역별로는 각각 DPPH와 ABTS의 경우 노랑물봉선, TPC의 경우 가야물봉선, 노랑물봉선, 물봉선, TFC의 경우 처진물봉선이 지역별 차이를 보였다. 이번 연구 결과를 토대로 국내 자생하는 물봉선속 분류군 별 항산화활성과 생리활성물질 차이를 확인할 수 있었고 추후 유용 소재로써의 이용과 우수 개체선발에 도움이 될 것으로 사료 된다.