• 한국조경학회지
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• 제27권4호
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• pp.131-136
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• 1999

Aerial videography techniques have been used to inventory conditions associated with grassland, forests, and agricultural crop production. Most recently, aerial videography has been used to verity satellite image classifications as part of the natural ecosystem survey. The objectives of this study were: (1) to use aerial video images of the study area, one part of Taean Seashore National Park, for the accuracy assessment, and (2) to determine the suitability of aerial videography as an accuracy assessment, of the land cover classification with Landsat-TM data. Video images were collected twice, summer and winter seasons, and divided into two kinds of images, wide angle and narrow angle images. Accuracy assessment methods include the calculation of the error matrix, the overall accuracy and kappa coefficient of agreement. This study indicates that aerial videography is an effective tool for accuracy assessment of the satellite image classifications of which features are relatively large and continuous. And it would be possible to overcome the limits of the present natural ecosystem survey method.

• Journal of the Korean Data and Information Science Society
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• 제25권6호
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• pp.1521-1530
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• 2014

The ROC analysis is considered for multiple class diagnosis. There exist many criteria to find optimal thresholds and measure the accuracy of diagnostic tests for k dimensional ROC analysis. In this paper, we proposed a diagnostic accuracy measure called the correct classification simple rate, which is defined as the summation of true rates for each classification distribution and expressed as a function of summation of sequential true rates for two consecutive distributions. This measure does not weight accuracy across categories by the category prevalence and is comparable across populations for multiple class diagnosis. It is found that this accuracy measure does not only have a relationship with Kolmogorov - Smirnov statistics, but also can be represented as a linear function of some optimal threshold criteria. With these facts, the suggested measure could be applied to test for comparing multiple distributions.

• 인터넷정보학회논문지
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• 제19권3호
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• pp.35-42
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• 2018

딥 러닝은 영상 분류를 위한 여러 방법 중 높은 정확도를 보이는 방법으로 알려져 있다. 본 논문에서는 딥 러닝 방법 가운데 합성곱 신경망 (CNN:Convolutional Neural Network)을 이용하여 영상을 분류함에 있어 배치 정규화 방법이 추가된 CNN을 이용하여 영상 분류의 정확도를 높이는 방법을 제시하였다. 본 논문에서는 영상 분류를 더 정확하게 수행하기 위해 기존의 뉴럴 네트워크에 배치 정규화 계층 (layer)를 추가하는 방법을 제안한다. 배치 정규화는 각 계층에 존재하는 편향을 줄이기 위해 고안된 방법으로, 각 배치의 평균과 분산을 계산하여 이동시키는 방법이다. 본 논문에서 제시된 방법의 우수성을 입증하기 위하여 SHREC13, MNIST, SVHN, CIFAR-10, CIFAR-100의 5개 영상 데이터 집합을 이용하여 영상분류 실험을 하여 정확도와 mAP를 측정한다. 실험 결과 일반적인 CNN 보다 배치 정규화가 추가된 CNN이 영상 분류 시 보다 높은 분류 정확도와 mAP를 보임을 확인 할 수 있었다.

• Spatial Information Research
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• 제12권1호
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• pp.111-125
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• 2004

위성영상 분류에 관한 주요 주제 중 하나는 분류 정확도 향상에 있다. 동일지역에 대한 동일시기의 위성영상을 취득할 수 있는 기회가 많아지는 현실을 감안할 때, 복수의 위성영상 데이터를 이용하여 분류정확도가 향상된 분류결과를 도출하는 것은 의미 있는 일일 것이다. 본 연구 주제는 최대우도법을 사용하여 계산된 데이터의 사후확률 및 분류 불확실도를 Dempster-Shafer의 증거이론에 적용하여 분류정확도를 향상시키고자 하는 것이다. 분석결과 개별적인 데이터 분류나 데이터간 융합에 의한 분류보다 본 연구에서 제안한 방법이 전체정확도와 Kappa 지수 모두 높은 정확도를 나타냈으며, 정확도 차에 대한 검정을 실시하여 본 연구에서 제안한 방법이 다른 방법에 비해 우수함을 통계적으로 증명하였다.

• 대한원격탐사학회지
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• 제31권4호
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• pp.303-319
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• 2015

The objectives of this paper are to measure surface imperviousness using three different classification methods: per-pixel, sub-pixel, and object-oriented classification. They are tested on high-spatial resolution QuickBird data at 2.4 meters (four spectral bands and three principal component bands) as well as a medium-spatial resolution Landsat TM image at 30 meters. To measure impervious surfaces, we selected 30 sample sites with different land uses and residential densities across image representing the city of Phoenix, Arizona, USA. For per-pixel an unsupervised classification is first conducted to provide prior knowledge on the possible candidate spectral classes, and then a supervised classification is performed using the maximum-likelihood rule. For sub-pixel classification, a Linear Spectral Mixture Analysis (LSMA) is used to disentangle land cover information from mixed pixels. For object-oriented classification several different sets of scale parameters and expert decision rules are implemented, including a nearest neighbor classifier. The results from these three methods show that the object-oriented approach (accuracy of 91%) provides more accurate results than those achieved by per-pixel algorithm (accuracy of 67% and 83% using Landsat TM and QuickBird, respectively). It is also clear that sub-pixel algorithm gives more accurate results (accuracy of 87%) in case of intensive and dense urban areas using medium-resolution imagery.

• 대한원격탐사학회지
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• 제34권1호
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• pp.141-150
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• 2018

Crop type classification is essential for supporting agricultural decisions and resource monitoring. Remote sensing techniques, especially using hyperspectral imagery, have been effective in agricultural applications. Hyperspectral imagery acquires contiguous and narrow spectral bands in a wide range. However, large dimensionality results in unreliable estimates of classifiers and high computational burdens. Therefore, reducing the dimensionality of hyperspectral imagery is necessary. In this study, the Random Forest (RF) classifier was utilized for dimensionality reduction as well as classification purpose. RF is an ensemble-learning algorithm created based on the Classification and Regression Tree (CART), which has gained attention due to its high classification accuracy and fast processing speed. The RF performance for crop classification with airborne hyperspectral imagery was assessed. The study area was the cultivated area in Chogye-myeon, Habcheon-gun, Gyeongsangnam-do, South Korea, where the main crops are garlic, onion, and wheat. Parameter optimization was conducted to maximize the classification accuracy. Then, the dimensionality reduction was conducted based on RF variable importance. The result shows that using the selected bands presents an excellent classification accuracy without using whole datasets. Moreover, a majority of selected bands are concentrated on visible (VIS) region, especially region related to chlorophyll content. Therefore, it can be inferred that the phenological status after the mature stage influences red-edge spectral reflectance.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.89-93
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• 2009

Embedded systems are becoming more popular as many embedded platforms have become more affordable. It offers a compact solution for many different problems including computer vision applications. Texture classification can be used to solve various problems, and implementing it in embedded platforms will help in deploying these applications into the market. This paper proposes to deploy the texture classification algorithms onto the embedded computer vision (ECV) platform. Two algorithms are compared; grey level co-occurrence matrices (GLCM) and Gabor filters. Experimental results show that raw GLCM on MATLAB could achieves 50ms, being the fastest algorithm on the PC platform. Classification speed achieved on PC and ECV platform, in C, is 43ms and 3708ms respectively. Raw GLCM could achieve only 90.86% accuracy compared to the combination feature (GLCM and Gabor filters) at 91.06% accuracy. Overall, evaluating all results in terms of classification speed and accuracy, raw GLCM is more suitable to be implemented onto the ECV platform.

• 한국컴퓨터정보학회논문지
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• 제18권1호
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• pp.177-183
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• 2013

본 논문에서는 데이터 마이닝에서 분류 작업을 실시할 때 그 분류정확도을 측정하기 위해 Cohen의 kappa 계수와 weighted kappa 계수를 제안하였다. kappa 계수는 우연에 의해 생기는 분류를 보정하여 분류정확도을 측정하며 명목척도와 순서척도의 데이터에 대해 사용된다. 특히 순서척도의 데이터에서는 오분류의 크기를 가중치에 의해 정량화하여 분류정확도을 측정하는 weighted kappa 계수가 더 유용하게 사용된다. weighted kappa 계수 계산을 위해서는 2가지 가중치(일차형 가중치, 이차형 가중치)를 사용하였다.. 또한 실제 데이터인 지방간 데이터에 대해 kappa 계수와 weighted kappa 계수를 계산하여 비교하였다.

• Geomechanics and Engineering
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• 제37권6호
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• pp.539-554
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• 2024

This study explores development of prediction model for seismic site classification through the integration of machine learning techniques with horizontal-to-vertical spectral ratio (HVSR) methodologies. To improve model accuracy, the research employs outlier detection methods and, synthetic minority over-sampling technique (SMOTE) for data balance, and evaluates using seven machine learning models using seismic data from KiK-net. Notably, light gradient boosting method (LGBM), gradient boosting, and decision tree models exhibit improved performance when coupled with SMOTE, while Multiple linear regression (MLR) and Support vector machine (SVM) models show reduced efficacy. Outlier detection techniques significantly enhance accuracy, particularly for LGBM, gradient boosting, and voting boosting. The ensemble of LGBM with the isolation forest and SMOTE achieves the highest accuracy of 0.91, with LGBM and local outlier factor yielding the highest F1-score of 0.79. Consistently outperforming other models, LGBM proves most efficient for seismic site classification when supported by appropriate preprocessing procedures. These findings show the significance of outlier detection and data balancing for precise seismic soil classification prediction, offering insights and highlighting the potential of machine learning in optimizing site classification accuracy.

• 대한원격탐사학회지
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• 제37권3호
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• pp.409-418
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• 2021

토지피복도는 국토정책, 환경정책을 위한 의사결정 근거 자료로 활용되는 매우 중요한 자료이다. 토지피복도는 원격탐사 자료를 활용하여 제작되는데, 이때 사용되는 데이터의 취득 시기에 따라 동일한 지역을 대상으로 하더라도 분류 결과가 달라질 수 있다. 본 연구에서는 단시기 데이터의 분류 정확도를 개선하기 위해 다중시기 위성영상을 활용하였으며 계절에 따른 지표면의 분광 반사 특성 차이를 딥러닝 알고리즘의 하나인 U-Net 모델에 학습시켜 분류하였다. 또한 단시기 분류 결과와 정확도 비교를 통해 분류 정확도의 향상 정도를 비교하였다. 구역 내에 30%의 녹지와 한강을 포함하여 다양한 토지피복으로 이루어진 서울특별시를 연구대상지로 설정하고 2020년 분기별 Sentinel-2 위성영상을 산출하였다. 대한민국 환경부에서 작성한 세분류 토지피복도를 활용하여 U-Net 모델을 학습시켰다. 학습한 U-Net 모델을 통해 단시기, 2시기, 3시기, 4시기로 모델을 학습하여 분류한 결과, 단시기를 제외하고 토지피복도 분류 정확도 확보기준인 75%를 상회하는 81%, 82% 79%의 정확도를 나타냈다. 이를 통해 다중 시계열 학습을 통해 토지피복의 분류 정확도 향상이 가능하다는 것을 확인하였다.