• 대한원격탐사학회지
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• 제24권5호
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• pp.473-481
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• 2008

This paper presents an approach to Back-propagation and Radial Basis Function neural network method with various training set for automatic vehicle detection from aerial images. The initial extraction of candidate object is based on Mean-shift algorithm with symmetric property of a vehicle structure. By fusing the density and the symmetry, the method can remove the ambiguous objects and reduce the cost of processing in the next stage. To extract features from the detected object, we describe the object as a log-polar shape histogram using edge strengths of object and represent the orientation and distance from its center. The spatial histogram is used for calculating the momentum of object and compensating the direction of object. BPNN and RBFNN are applied to verify the object as a vehicle using a variety of non-car training sets. The proposed algorithm shows the results which are according to the training data. By comparing the training sets, advantages and disadvantages of them have been discussed.

• 대한원격탐사학회지
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• 제20권5호
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• pp.353-359
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• 2004

This study proposes an approach to unsupervisedly estimate the number of classes and the parameters of defining the classes in order to train the classifier. In the proposed method, the image is segmented using a spatial region growing based on hierarchical clustering, and fuzzy training is then employed to find the sample classes that well represent the ground truth. For cluster validation, this approach iteratively estimates the class-parameters in the fuzzy training for the sample classes and continuously computes the log-likelihood ratio of two consecutive class-numbers. The maximum ratio rule is applied to determine the optimal number of classes. The experimental results show that the new scheme proposed in this study could be used to select the regions with different characteristics existed on the scene of observed image as an alternative of field survey that is so expensive.

• 실천공학교육논문지
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• 제15권2호
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• pp.299-309
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• 2023

본 연구는 코로나19 위기 상황에 대처하기 위하여 직업훈련 사업별로 단선적으로 이루어져 왔던 원격훈련 도입 관련 제도 운영 실태에 대하여 심사에서부터 비용집행에 이르기까지의 과정을 주체별, 단계별, 절차별로 구분하여 문제점과 한계점을 분석하였다. 이후 이해관계자들의 의견을 다각적으로 수렴하여, 디지털·비대면 시대의 직업훈련 패러다임 전환에 부응할 수 있는 직업훈련 지원체제의 개선 방안을 제시하였다. 특히 기존 전통적인 집체훈련 중심의 프레임에서 벗어나 디지털·비대면 시대의 직업훈련 방향성을 수용할 수 있는 방향으로 훈련기관, 훈련과정의 심사·평가 제도가 혁신되어야 한다는 기본 전제하에, 사전 승인 심사 제도와 훈련기관 인증평가 제도의 개선방안을 제시하였다.

• 대한원격탐사학회지
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• 제24권5호
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• pp.507-515
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• 2008

Many studies have generally used a large number of pure pixels as an approach to training set design. The training set are used, however, varies between classifiers. In the recent research, it was reported that small mixed pixels between classes are actually more useful than larger pure pixels of each class in Support Vector Machine (SVM) classification. We evaluated a usability of small mixed pixels as a training set for the classification of high-resolution satellite imagery. We presented an advanced approach to obtain a mixed pixel readily, and evaluated the appropriateness with the land cover classification from IKONOS satellite imagery. The results showed that the accuracy of the classification based on small mixed pixels is nearly identical to the accuracy of the classification based on large pure pixels. However, it also showed a limitation that small mixed pixels used may provide insufficient information to separate the classes. Small mixed pixels of the class border region provide cost-effective training sets, but its use with other pixels must be considered in use of high-resolution satellite imagery or relatively complex land cover situations.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.214-222
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• 2022

Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2017년도 추계학술대회
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• pp.751-753
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• 2017

본 논문은 AVR 실습키트를 원격으로 실습할 수 있도록 카메라, 아두이노, AVR 실습키트를 이용하여 원격실습 키트를 구현한다. 구현된 시스템은 원격지에서 한번에 한사람씩 다수의 사용자가 실습을 수행할 수 있다. 실습자는 PC의 원격제어 방법을 이용하여 AVR Studio 프로그램을 작성하고 AVR 실습키트에 다운로드 한다. 그리고, 버튼 입력, 가변저항 입력은 컴퓨터 프로그램을 작성하여 마우스를 클릭하거나 드래그 하면 입력 신호는 아두이노에 전달되고 아두이노는 실제 버튼 입력신호나 아날로그 전압을 AVR 키트에 전달한다. 입력 신호를 받아서 AVR 키트가 동작하면 카메라를 통해서 동작 모습을 확인 할 수 있다. 따라서 구현된 시스템을 이용하면 다수의 사용자가 하나의 키트를 이용하여 AVR 실습을 수행할 수 있다.

• Spatial Information Research
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• 제2권2호
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• pp.175-188
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• 1994

본 연구에서는 최근 많은 분야데서 패턴인식을 위한 효과적인 기법으로 이용되고 있는 신경망 기법을 원격탐사자료의 군집화 기법으로서 적용하고자 하였다. 이를 위해 선택된 신경망 모델은 경쟁학습 신경망이며 이를 구성하는 각종 변수들을 재구성하여 원격탐사자료의 군집화를 위한 신경망모델을 설정하였다. 본 신경망을 이용한 군집화 기법은 항공기를 이용하여 획득된 원격탐사자료를 이용하여 순차적(sequential)군집화 기법 K 평균 군집화 기법과 비교되었다. 계산시간은 순차적 기법이나 K 평균기법에 비하여 더 많이 소요되나 정확도면에 있어서는 비교적 우수한 결과를 나타냈다.

• 대한원격탐사학회지
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• 제39권4호
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• pp.395-407
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• 2023

Super-resolution (SR) has great significance in image processing because it enables downstream vision tasks with high spatial resolution. Recently, SR studies have adopted deep learning networks and achieved remarkable SR performance compared to conventional example-based methods. Deep-learning-based SR models generally require low-resolution (LR) images and the corresponding high-resolution (HR) images as training dataset. Due to the difficulties in obtaining real-world LR-HR datasets, most SR models have used only HR images and generated LR images with predefined degradation such as bicubic downsampling. However, SR models trained on simple image degradation do not reflect the properties of the images and often result in deteriorated SR qualities when applied to real-world images. In this study, we propose an image degradation model for HR satellite images based on the modulation transfer function (MTF) of an imaging sensor. Because the proposed method determines the image degradation based on the sensor properties, it is more suitable for training SR models on remote sensing images. Experimental results on HR satellite image datasets demonstrated the effectiveness of applying MTF-based filters to construct a more realistic LR-HR training dataset.

• 대한원격탐사학회지
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• 제38권4호
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• pp.375-386
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• 2022

The city of Khartoum, the capital of Sudan, was heavily damaged by the flood of the Nile in 2020. Classification using satellite images can define the damaged area and help emergency response. As Synthetic Aperture Radar (SAR) uses microwave that can penetrate cloud, it is suitable to use in the flood study. In this study, Random Forest classifier, one of the supervised classification algorithms, was applied to the flood event in Khartoum with various sizes of the training dataset and number of images using Sentinel-1 SAR. To create a training dataset, we used unsupervised classification and visual inspection. Firstly, Random Forest was performed by reducing the size of each class of the training dataset, but no notable difference was found. Next, we performed Random Forest with various number of images. Accuracy became better as the number of images in creased, but converged to a maximum value when the dataset covers the duration from flood to the completion of drainage.

• 환경영향평가
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• 제3권1호
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• pp.77-82
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• 1994

A Nationwide survey of ecosystem in the Republic of Korea was accomplished from 1986 to 1990 and in that survey, GIS and remote sensing were used partially. This was done by the Ministry of Environment(MOE), which introduced remote sensing and GIS for environment management in late 1980's. Especially the National Institute of Environmental Research (NIER) are under the research on systematization of environmental information with an ultimate goal of application of GIS and remote sensing to environmental impact assessment. Although the Korean peninsula is in a non-tropical zone, we introduce two case studies on remote sensing applications to ecosystem managements in the Republic of Korea. One is a study on change detection in urban vegetation of Seoul with Landsat data and the other is a study on detection of insect damaged pine tree area using Landsat TM data. The techniques involved and the conclusion from these studies were relevant to vegetation studies in tropical ecosystem.