• 대한원격탐사학회지
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• 제36권5_2호
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• pp.907-916
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• 2020

Images taken from airplanes, satellites and drones have been used in various realms, and the kinds and specifications of images are enlarged gradually. Despite the importance of images on diverse applications, the quality information of the images is controlled by each agency or institute respectively without any principle, or even is neglected, because the application of standards to the final products of image is not easy in Korea. We aim to review necessities and strategies for applying international standards on image and to suggest potential issues and possibilities to make standards in action.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.455-458
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• 2006

Stretching on the coastline of 70 km, the Tam Giang - Cau Hai Lagoon plays a very important role for the coastal ecology and socio-economic development of Hue region where was Vietnam's Ancient Kingdom Capital and recognized as a World's Cultural Heritage. Recently, coastal hazard in the lagoon have occurred seriously such as inlet movement and fill up, coastal erosion, flood and inundation, etc. These hazards have impacted on lagoon environment, resources, ecosystems, socio-economic and sustainable development of this coastal area. This paper present a case study using remote sensing data in combination with ground survey for monitoring the coastal hazards in Tam Giang - Cau Hai lagoon in recent decades. Analysis results find that during its natural evolution, the lagoon has been being in three situations of only one, two and three inlets. When inlets opened or displaced, coastal erosion have occurred seriously toward new balance condition. Flood and inundation occurs every rainy season in lowland plain around lagoon. The historical flood happened in early of November 1999 with six days long, created very terrible damages for Thua Thien Hue province. Remote sensing data with capability of regular update, large area coverage is effective provide real-time and continuous information for coastal hazards monitoring.

• 대한원격탐사학회지
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• 제35권6_2호
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• pp.1067-1082
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• 2019

본 논문은 현재 산림 분야 연구에 적용되고 향후 적용가능한 원격탐사 기술에 대한 국내외 발행된 peer-reviewed 논문의 리뷰를 바탕으로 원격탐사 기술의 국내 산림분야 적용에 대한 가능성과 한계점을 서술하였다. 원격탐사 기술은 정밀한 분석과 정교한 자료 수집을 바탕으로 대단위 산림면적 분석에 있어 필수적이며, 정보통신기술과의 융합으로 향후 임업의 새로운 시대를 열어갈 핵심 기술이다. 본 리뷰 논문에서는 다양한 원격탐사 기술 가운데 레이저 스캐닝 기술, 위성영상을 이용한 산림 측정 기술, 그리고 무인항공기를 이용한 기존 국내·외 연구사례를 분석하여 국내 산림분야 적용 가능성에 대한 기회와 한계점에 대해 서술하였다.

• 한국농공학회논문집
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• 제58권5호
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• pp.29-37
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• 2016

Unmanned Aerial Vehicle (UAV) have several advantages over conventional remote sensing techniques. UAV can acquire high-resolution images quickly and repeatedly with a comparatively lower flight altitude i.e. 80~400 m nullifying the effect of extreme weather and cloud. This study discussed the use of low cost-effective UAV based remote sensing application in inspection of agricultural subsidy. The study area accrue $60.5km^2$ of Buljeong-myeon, Goesan-gun, Chungbuk in South Korea. UAV image acquired 25 times from July 25 to August 11, 2015 for 3 days. It is observed that almost 81.1 % (3,571 of 4,410 parcels) parcels are truthful whereas some parcels are incorrect or fraudulent. Surveying with UAV for agricultural subsidy instead of field stuff can reduce the required time as much as 64.8 % (19 of 54 days). Therefore, it can contribute significantly in speedy and more accurate processing of grant application and can end unfair receipt of the grant which in turn will improve customer satisfaction.

• 한국소음진동공학회논문집
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• 제26권6_spc호
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• pp.651-659
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• 2016

Condition monitoring (CM) encounters a large data problem due to sensors that measure vibration data with a continuous, and sometimes, high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate the efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer samples compared to traditional sampling methods. For the experiments a built-in rotating system was used and all data were compressively sampled to obtain compressed data. Optimal signal features were then selected without the reconstruction process and were used to detect and classify damage. The experimental results show that the proposed method could improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• 한국통신학회논문지
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• 제31권2B호
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• pp.107-116
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• 2006

본 논문에서는 무선 센서 네트워크에서 센싱 정밀도에 기반 한 그룹화 통신 프로토콜 (SRG)을 제안한다. SRG 는 센서 노드가 밀집해 있는 네트워크에서 응용계층이 요구하는 센싱 정밀도를 만족시키는 방법을 제공하는데, 그룹 헤더 노드를 매 round 마다 라운드-로빈 방식으로 교체하여 전반적인 센싱 정밀도를 높이고 각 노드가 소모하는 에너지를 비슷하게 되도록 만든다. 또한 라우팅 시에 소모되는 에너지를 줄이기 위해 그룹의 크기와 에너지 소모를 고려한 중계 노드를 선택 방법 제안하여 에너지 효율적인 통신이 이뤄지도록 한다. 성능평가를 위한 모의실험 결과 제안한 SRG 프로토콜은 센서노드들의 에너지 소비량을 줄이고 네트워크의 생존시간을 늘리는 것을 알 수 있다.

• 대한원격탐사학회지
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• 제37권4호
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.406-412
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• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(5)
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• pp.147-150
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• 2000

Manual and predictive defrosting method is used in current refrigerators, which have several problems in terms of energy consumption and efficiency. fuming the defrosting system on by the amount of frost remains to be an important problem which has to be improved by refrigerator manufacturers. The sensing of the amount of frost by FDS(Frost Detecting Sensor) and its proper mounting point are investigated in the paper. Also the realization of actual defrosting system through experiments of operation, energy consumption and sensing mechanism is presented.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.210-212
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• 2003

Active sensors are rapidly conquering a share on the remote sensing market and offer among others new possibilities toward automatically acquiring 3D building data. Better dissemination of information about new technological developments can possibly be achieved by short distance-learning courses. The paper describes the didactic and technical aspects of a course we have designed and conducted on airborne laser scanning and interferometric SAR. The building extraction application is a good example to illustrated the added value of short electronic-learning courses above simply publishing (digital) papers.