최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기한국측량학회지 = Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.37 no.6, 2019년, pp.405 - 416
서홍덕 (Department of Spatial Information Engineering, Namseoul University) , 김의명 (Department of Spatial Information Engineering, Namseoul University)
Due to the development of information and communication technology, the production and processing speed of data is getting faster. To classify objects using machine learning, which is a field of artificial intelligence, data required for training can be easily collected due to the development of int...
* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.
핵심어 | 질문 | 논문에서 추출한 답변 |
---|---|---|
머신러닝이란? | 머신러닝은 인공지능의 한 분야로 데이터와 이 데이터로부터 기대되는 해답을 입력하여 규칙을 알아내는 것으로 이를 위해서는 입력된 데이터를 학습시키는 것이 중요하다. 공간정보 분야에서 머신러닝은 영상, 포인트 클라우드(point cloud) 등을 이용하여 객체를 분류 또는 인식하는 것에 적용되고 있다(Liu, 2015; Hong, 2017; Lee and Yom, 2018) | |
RGB 밴드 기반의 객체 분류 연구들의 문제점은 무엇인가? | , 2019) 등이 수행되었다. 그러나 이러한 연구들은 RGB 밴드에 의존하여 객체를 분류하기 때문에 다양한 조명 조건 및 환경으로 인하여 오분류되는 결과가 발생할 수 있는 문제점이 있었다(Liu and Xia, 2010). 또한, 개별 화소를 객체분류의 기본 단위로 사용하기 때문에 노이즈가 민감하고, RGB (Red, Green, Blue) 밴드 기반 분류를 수행할 시 동일한 분류 내에서 큰 변동성을 발생시켜 낮은 분류정확도를 갖는 한계가 존재하였다(Schöpfer et al., 2010; Liu and Xia, 2010). | |
공간정보 분야에서 머신러닝은 어떻게 적용되나? | 머신러닝은 인공지능의 한 분야로 데이터와 이 데이터로부터 기대되는 해답을 입력하여 규칙을 알아내는 것으로 이를 위해서는 입력된 데이터를 학습시키는 것이 중요하다. 공간정보 분야에서 머신러닝은 영상, 포인트 클라우드(point cloud) 등을 이용하여 객체를 분류 또는 인식하는 것에 적용되고 있다(Liu, 2015; Hong, 2017; Lee and Yom, 2018) |
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., and Lloyd, S. (2017), Quantum machine learning. Nature. Vol. 549, pp. 195-202.
Cho, D.Y. and Kim, E.M. (2010), Extraction of spatial information of tree using LIDAR data in urban area. The Journal of Korean Society for Geospatial Information Science, Vol. 18, No. 4, pp. 11-20. (in Korean with English abstract)
Choi, S.P., Yang, I.T., and Cong, J.H. (2002), Evaluation of horizontal position accuracy of facilities in digital map. The Journal of Korean Society for Geospatial Information Science, Vol. 10, No. 4, pp. 95-103. (in Korean with English abstract)
Daneshtalab, S. and Rastiveis, H. (2017), Decision level fusion of orthophoto and LIDAR data using confusion matrix information for land cover classification, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W4, 2017 Tehran's Joint ISPRS Conferences of GI Research, SMPR and EOEC 2017, 7-10 October, Tehran, Iran, pp. 59-64.
Feng, Q., Liu, J., and Gong, J. (2015), UAV remote sensing for urban vegetation mapping using random forest and texture. Remote Sensing, Vol. 7, No. 1, pp. 1074-1094.
Han, S.H. (2016), Introduction to Photogrammetry and Remote Sensing, Goomibook, Seoul.
Hong, I.Y. (2017), Land use classification using LBSN(Location-Based Social Network) data and machine learning technique, Journal of the Korean Cartographic Association, Vol. 17, No. 3, pp. 59-67. (in Korean with English abstract)
Hong, S.P. and Kim, E.M. (2018), Classification of 3D road objects using machine learning. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 36, No. 6, pp. 535-544. (in Korean with English abstract)
Jeong, D.H. and Jeong, W.T. (2019), Prediction of rolling noise based on machine learning technique using rail surface roughness data, Journal of the Korean Society for Railway, Vol. 22. No. 3, pp. 209-217. (in Korean with English abstract)
Jo, W.H., Lim, Y.H., and Park, K.H. (2019), Deep learning based land cover classification using convolutional neural network: a case study of Korea. Journal of the Korean Geographical Society, Vol. 54, No. 1, pp. 1-16. (in Korean with English abstract)
Jonsson, Sigurbjorn. (2019), RGB and multispectral UAV image classification of agricultural fields using a machine learning algorithm, Master's thesis, Lund University, Lund, Sweden, 88p.
Kim, G.M. and Choi, J.W. (2018), Detection of cropland in reservoir area by using supervised classification of UAV imagery based on GLCM. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 36, No. 6, pp. 433-442. (in Korean with English abstract)
Kim, E.M., Cho, H.S., and Park, J.H. (2017), Analysis of applicability of orthophoto using 3D mesh on aerial image with large file size, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 35, No. 3, pp. 155-166. (in Korean with English abstract)
Kim, J.K., Lee, K.B., and Hong, S.G. (2017), ECG-based biometric authentication using random forest. Journal of the Institute of Electronics and Information Engineers, Vol. 54, No. 6, pp. 100-105. (in Korean with English abstract)
Kwon, S.K., Lee, Y.S., Kim, D.S., and Jung, H.S. (2019), Classification of forest vertical structure using machine learning analysis, Korean Journal of Remote Sensing, Vol. 35, No. 2, pp. 229-239. (in Korean with English abstract)
Lee, G.W. and Yom, J.H. (2018), Design and implementation of web-based automatic preprocessing system of remote sensing imagery for machine learning modeling. The Journal of Korean Society for Geospatial Information Science, Vol. 26, No. 1, pp. 61-67. (in Korean with English abstract)
Liu, D. and Xia, F. (2010), Assessing object-based classification: advantages and limitations, Remote Sensing, Vol. 1, No. 4, pp. 187-194.
Liu, P. (2015), A survey of remote-sensing big data. Frontiers in Environmental Science, Vol. 3, No. 45, pp. 1-6.
Park, G.M. and Bae, Y.C. (2019), Performance comparison of machine learning in the various kind of prediction. The Journal of the Korea Institute of Electronic Communication Sciences, Vol. 14, No. 1, pp. 169-178. (in Korean with English abstract)
Park, H.K. and Lee, D.K. (2019), Disaster prediction and policy simulation for evaluating mitigation effects using machine learning and system dynamics: case study of seasonal drought in gyeonggi province. Journal of the Korean Society of Hazard Mitigation, Vol. 19, No. 1, pp. 45-53. (in Korean with English abstract)
Park, S., Kim, K.J., Lee, J.S., and Lee, S.R. (2011), Red tide prediction using neural network and SVM, The Institute of Electronics Engineers of Korea-Signal Processing, Vol. 48. No. 5, pp. 39-45. (in Korean with English abstract)
Schonberger, J.L. and Frahm, J.M. (2016), Structure-from-motion revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 27-30 June, Las Vegas, USA, pp. 4104-4113.
Schopfer, E., Lang, S., and Strobl, J. (2010), Segmentation and object-based image analysis. Remote sensing of urban and suburban areas, Vol. 10, pp. 181-192.
Wang, C. and Li, Z. (2016), Weed recognition using SVM model with fusion height and monocular image features. Transactions of the Chinese Society of Agricultural Engineering, Vol. 32, No. 15, pp. 181-192.
Wikipedia. (2019), Random forest, Wikimedia Foundation, Inc., URL: https://en.wikipedia.org/wiki/Random_forest(last date accessed: 26 June 2019).
Yamamoto, K., Guo, W., Yoshioka, Y., and Ninomiya, S. (2014). On plant detection of intact tomato fruits using image analysis and machine learning methods. Sensors, Vol. 14, No. 7, pp. 12191-12206.
Yu, B.H., P, H.C., and Lee, S.M. (2019), Improvement of randomforest OBIA algorithm for tree anomaly detection in UAV imagery: Focused on the Birobong-Peak Area of Sobaeksan National Park. The Korean Society of Environment and Ecology, 26 April, Wonju, Korea, pp. 54-54.
Yun, T.G. and Yi, G.S. (2008), Application of random forest algorithm for the decision support system of medical diagnosis with the selection of significant. The transactions of The Korean Institute of Electrical Engineers, Vol. 57, No. 6, pp. 1058-1062. (in Korean with English abstract)
Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G. (2016), An easy-to-use airborne LiDAR data filtering method based on cloth simulation, Remote Sensing, Vol. 8, No. 6, pp. 501.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.