• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.97-102
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• 2017

Recently deep learning techniques such as convolutional neural networks (CNN) have been introduced to classify high-resolution remote sensing data. In this paper, we investigated the possibility of applying CNN to crop classification of farmland images captured by drones. The farming area was divided into seven classes: rice field, sweet potato, red pepper, corn, sesame leaf, fruit tree, and vinyl greenhouse. We performed image pre-processing and normalization to apply CNN, and the accuracy of image classification was more than 98%. With the output of this study, it is expected that the transition from the existing image classification methods to the deep learning based image classification methods will be facilitated in a fast manner, and the possibility of success can be confirmed.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.469-472
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• 2011

본 논문에서는 모바일 기기의 디스플레이 비율을 고려한 복합적 Image retargeting 기법을 제안한다. 제안 기법은 다양한 비율을 가지는 광시야각 영상을 모바일기기에서 디스플레이하고자 할 때 줄어드는 과정에서 영상 내의 주요 영역의 손실을 최소화하고 자연스러운 결과 영상을 만들어내기 위해 영상의 saliency를 분석하여 에너지 분포와 gravity center를 구한다. 분석된 에너지 분포를 통해 영상을 동일한 에너지량을 가진 n개의 영역으로 분할하고 각 영역의 분포 특징에 따라 crop, linear scaling, seam carving의 기법을 최적의 영역에 복합적으로 적용하여 영상을 retargeting한다. 끝으로 제안하는 기법의 결과영상과 기존 기법의 결과 영상을 비교하여 제안 기법의 강점을 검증한다.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.265-271
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• 2015

This study proposes an algorithm for recognizing apple trees in images and detecting apples to measure the number of apples on the trees. The proposed algorithm explores whether there are apple trees or not based on the number of image block-unit edges, and then it detects apple areas. In order to extract colors appropriate for apple areas, the CIE $L^*a^*b^*$ color space is used. In order to extract apple characteristics strong against illumination changes, modified census transform (MCT) is used. Then, using the AdaBoost learning algorithm, characteristics data on the apples are learned and generated. With the generated data, the detection of apple areas is made. The proposed algorithm has a higher detection rate than existing pixel-based image processing algorithms and minimizes false detection.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.3
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• pp.162-178
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• 2017

Remote sensing can be used to provide information about the monitoring of crop growth condition. Recently Unmanned Aerial Vehicle (UAV) technology offers new opportunities for assessing crop growth condition using UAV imagery. The objective of this study was to assess weather UAV aerial images are suitable for the monitoring of highland Kimchi cabbage. This study was conducted using a fixed-wing UAV (Model : Ebee) with Cannon S110, IXUS/ELPH camera during farming season from 2015 to 2016 in the main production area of highland Kimchi cabbage, Anbandegi, Maebongsan, and Gwinemi. The Normalized Difference Vegetation Index (NDVI) by using UAV images was stable and suitable for monitoring of Kimchi cabbage situation. There were strong relationships between UAV NDVI and the growth parameters (the plant height and leaf width) ($R^2{\geq}0.94$). The tendency of UAV NDVI according to Kimchi cabbage growth was similar in the same area for two years (2015~2016). It means that if UAV image may be collected several years, UAV images could be used for estimation of the stage of growth and situation of Kimchi cabbage cultivation.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.45-45
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• 2017

On-site monitoring of vegetable growth parameters, such as leaf length, leaf area, and fresh weight, in an agricultural field can provide useful information for farmers to establish farm management strategies suitable for optimum production of vegetables. Unmanned Aerial Vehicles (UAVs) are currently gaining a growing interest for agricultural applications. This study reports on validation testing of previously developed vegetable growth estimation models based on UAV-based RGB images for white radish and Chinese cabbage. Specific objective was to investigate the potential of the UAV-based RGB camera system for effectively quantifying temporal and spatial variability in the growth status of white radish and Chinese cabbage in a field. RGB images were acquired based on an automated flight mission with a multi-rotor UAV equipped with a low-cost RGB camera while automatically tracking on a predefined path. The acquired images were initially geo-located based on the log data of flight information saved into the UAV, and then mosaicked using a commerical image processing software. Otsu threshold-based crop coverage and DSM-based crop height were used as two predictor variables of the previously developed multiple linear regression models to estimate growth parameters of vegetables. The predictive capabilities of the UAV sensing system for estimating the growth parameters of the two vegetables were evaluated quantitatively by comparing to ground truth data. There were highly linear relationships between the actual and estimated leaf lengths, widths, and fresh weights, showing coefficients of determination up to 0.7. However, there were differences in slope between the ground truth and estimated values lower than 0.5, thereby requiring the use of a site-specific normalization method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.3 s.303
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• pp.73-82
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• 2005

Recently, The wide range of the Internet applications and the related technology developments enabled the ease use of the digital multimedia contents (fixed images, movies, digital audios). However, due to the replay ability which the contents may be easily duplicated and not only the duplicates are capable of providing the same original quality. There are mainly the encipher techniques and the watermarking techniques which are studied and used as solutions for the above problem in order to protect the license holders' rights. To the protection of the IP(Intellectual Property) rights of the owner, digital watermarking is the technique that authenticates the legal copyrighter. This paper proposed the watermarking algorithms to watermark the 256 gray logo image and the color image by applying the wavelet transformation to the color stand-still images. The proposed algorithms conducted the watermark insertion at the LH frequency region among the wavelet transformation regions (LL, LH, HL, HH). The interleaving algorithms which applied in data communication was applied to the watermark. the amount of watermark increased which consequently caused the PSNR to decrease but this might provide the perseverance against the external attacks such as extraction, filtering, and crop.

• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.383-401
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• 2016

In this paper, we described the statistical modeling of crop yields using satellite images, climatic datasets, soil property maps, and fertilizer data for the Midwestern United States during 2001-2012. Satellite images were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and climatic datasets were provided by the Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group. Soil property maps were derived from the Harmonized World Soil Database (HWSD). Our multivariate regression models produced quite good prediction accuracies, with differences of approximately 8-15% from the governmental statistics of corn and soybean yields. The unfavorable conditions of climate and vegetation in 2012 could have resulted in a decrease in yields according to the regression models, but the actual yields were greater than predicted. It can be interpreted that factors other than climate, vegetation, soil, and fertilizer may be involved in the negative biases. Also, we found that soybean yield was more affected by minimum temperature conditions while corn yield was more associated with photosynthetic activities. These two crops can have different potential impacts regarding climate change, and it is important to quantify the degree of the crop sensitivities to climatic variations to help adaptation by humans. Considering the yield decreases during the drought event, we can assume that climatic effect may be stronger than human adaptive capacity. Thus, further studies are demanded particularly by enhancing the data regarding human activities such as tillage, fertilization, irrigation, and comprehensive agricultural technologies.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.10
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• pp.1145-1156
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• 2015

There is a shortage of mobile application systems readily applicable to the field of crop cultivation in relation to diseases and insect pests directly connected to the quality of crops. Most of system have been devoted to diseases and insect pests that would offer full predictions and basic information about diseases and insect pests currently. But for lack of the instant diagnostic functions seriously and the field of crop cultivation, we design and implement a crop cultivation field-oriented smart diseases and insect pests information retrieval system based on mobile for u-Farm. The proposed system had such advantages as providing information about diseases and insect pests in the field of crop cultivation and allowing the users to check the information with their smart-phones real-time based on the Lucene, a search library useful for the specialized analysis of images, and JSON data structure. And it was designed based on object-oriented modeling to increase its expandability and reusability. It was capable of search based on such image characteristic information as colors as well as the meta-information of crops and meta-information-based texts. The system was full of great merits including the implementation of u-Farm, the real-time check, and management of crop yields and diseases and insect pests by both the farmers and cultivation field managers.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.86-101
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• 2019

Remote sensing technology has been continuously developed both quantitatively and qualitatively, including platform development, exploration area, and exploration functions. Recently, the use cases and related researches in the agricultural field are increasing. Also, since it is possible to detect and quantify the condition of cropland and establish management plans and policy support for cropland and agricultural environment, it is being studied in various fields such as crop growth abnormality determination and crop estimation based on time series information. The purpose of this study was to analyze the vegetation index for agricultural land reclamation area using a UAV equipped with a multi-spectral sensor. In addition, field surveys were conducted to evaluate the accuracy of vegetation indices calculated from multispectral image data obtained using UAV. The most appropriate vegetation index was derived by evaluating the correlation between vegetation index calculated by field survey and vegetation index calculated from UAV multispectral image, and was used to analyze vegetation index of the entire area.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.3-11
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• 2011

The objective of this study was to select appropriate sites for reforestation CDM using satellite image and spatial data in North Korea. A suitable site for reforestation CDM can be defined as non-forest area since 1990. To detect land cover change between two different years and to delineate potential reforestation CDM site(unstocked forest, converted crop land, denuded bare land), Landsat TM satellite image which was scanned in 1988 and SPOT Pan-sharpened image which was scanned in 2007 were used. As a result of classification, 1,214 ha of forest area in 1989 had been converted to other land cover types in 2007. and 2,245 ha of the total study area was detected to be suitable for reforestation CDM. 79.2% of total potential CDM sites was converted crop land. Through topography and accessibility analysis for potential reforestation CDM sites detected by satellite image, suitability index was calculated and the potential reforestation area was reclassified into suitability grades.