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NTIS 바로가기대한원격탐사학회지 = Korean journal of remote sensing, v.38 no.6 pt.2, 2022년, pp.1663 - 1676
백원경 (서울시립대학교 공간정보공학과) , 이명진 (한국환경연구원 환경데이터전략센터) , 정형섭 (서울시립대학교 공간정보공학과)
Recently, a number of deep-learning based land cover segmentation studies have been introduced. Some studies denoted that the performance of land cover segmentation deteriorated due to insufficient training data. In this study, we verified the improvement of land cover segmentation performance throu...
AI Hub, 2020. 2020 Satellite-derived landcover dataset, https://www.aihub.or.kr/aihubdata/data/list.do?pageIndex1&currMenu115&topMenu100&dataSetSn&srchdataClCodeDATACL001&srchOrder&SrchdataClCodeDATACL002&searchKeyword%ED%86%A0%EC%A7%80%ED%94%BC%EB%B3%B5, Accessed on Nov. 30, 2022.
Baek, W.-K. and H.-S. Jung, 2021a. Performance comparison of oil spill and ship classification from x-band dual-and single-polarized SAR image using support vector machine, random forest, and deep neural network, Remote Sensing, 13(16): 3203. https://doi.org/10.3390/rs13163203
Baek, W.-K., 2022, Phase Unwrapping Using Modified U-Net Regression Model: Focusing on Network Structure and Training Data Optimization, University of Seoul, Seoul, Korea (in Korean with English abstract).
Baek, W.-K., H.-S. Jung, and D. Kim, 2020. Oil spill detection of Kerch strait in November 2007 from dual-polarized TerraSAR-X image using artificial and convolutional neural network regression models, Journal of Coastal Research, 102(SI): 137-144. https://doi.org/10.2112/SI102-017.1
Baek, W.-K., S.-H. Park, N.-K. Jeong, S. Kwon, W.-J. Jin, and H.-S. Jung, 2017. A study for the techniques and applications of NIR remote sensing based on statical analyses of NIR-related papers, Korean Journal of Remote Sensing, 33(5-3): 889-900 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2017.33.5.3.11
Baek, W.-K., Y.-S. Lee, S.-H. Park, and H.-S. Jung, 2021b. Classification of Natural and Artificial Forests from KOMPSAT-3/3A/5 Images Using Deep Neural Network, Korean Journal of Remote Sensing, 37(6-3): 1965-1974 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.6.3.5
Halevy, A., P. Norvig, and F. Pereira, 2009. The unreasonable effectiveness of data, IEEE Intelligent Systems, 24: 8-12. https://doi.org/10.1109/MIS.2009.36
Jin, Y.W., S. Jia, A.B. Ashraf, and P. Hu, 2020. Integrative data augmentation with U-Net segmentation masks improves detection of lymph node metastases in breast cancer patients, Cancers, 12(10): 2934. https://doi.org/10.3390/cancers12102934
Johnson, J.M. and T.M. Khoshgoftaar, 2019. Survey on deep learning with class imbalance, Journal of Big Data, 6(1): 1-54. https://doi.org/10.1186/s40537-019-0192-5
Kim, M.J., S.M. Lee, J.C. Park, H.W. Lee, C.M. Kwon, and I.Y. Won, 2018. A Poisonous Plants Classification System Using Data Augmentation And Transfer Learning, Proc. of the Korea Information Processing Society Conference, Busan, Korea, Nov. 2-3, pp. 660-663.
Lee, S., W.-K. Baek, H.-S. Jung, and S. Lee, 2020. Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon, Applied Sciences, 10(22): 8189. https://doi.org/10.3390/app10228189
Lee, S.H. and M.J. Lee, 2021. A Study of Establishment and application Algorithm of Artificial Intelligence Training Data on Land use/cover Using Aerial Photograph and Satellite Images, Korean Journal of Remote Sensing, 37(5-1): 871-884 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.5.1.4
Liu, M., B. Fu, S. Xie, H. He, F. Lan, Y. Li, P. Lou, and D. Fan, 2021. Comparison of multi-source satellite images for classifying marsh vegetation using DeepLabV3 Plus deep learning algorithm, Ecological Indicators, 125: 107562. https://doi.org/10.1016/j.ecolind.2021.107562
Nazi, Z.A. and T. A. Abir, 2020. Automatic skin lesion segmentation and melanoma detection: Transfer learning approach with u-net and dcnn-svm, Proc. of International Joint Conference on Computational Intelligence. Budapest, Hungary, Nov. 2-4, pp. 371-381.
Oliveira, G.L., 2019. Encoder-decoder Methods for Semantic Segmentation: Efficiency and Robustness Aspects, Albert-Ludwigs-Universitat Freiburg, Freiburg, Germany.
Ronneberger, O., P. Fischer, and T. Brox, 2015. U-net: Convolutional networks for biomedical image segmentation, Proc. of 2015 International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, Oct. 5-9, pp. 234-241.
Shi, P., M. Duan, L. Yang, W. Feng, L. Ding, and L. Jiang, 2022. An improved U-net image segmentation method and its application for metallic grain size statistics, Materials, 15(13): 4417. https://doi.org/10.3390/ma15134417
Shorten, C. and T.M. Khoshgoftaar, 2019. A survey on image data augmentation for deep learning, Journal of Big Data, 6(1): 1-48. https://doi.org/10.1186/s40537-019-0197-0
Stoian, A., V. Poulain, J. Inglada, V. Poughon, and D. Derksen, 2019. Land cover maps production with high resolution satellite image time series and convolutional neural networks: Adaptations and limits for operational systems, Remote Sensing, 11(17): 1986. https://doi.org/10.3390/rs11171986
Vali, A., S. Comai, and M. Matteucci, 2020. Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review, Remote Sensing, 12(15): 2495. https://doi.org/10.3390/rs12152495
Yu, J.-W., Y.-W. Yoon, W.-K. Baek, and H.S. Jung, 2021. Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches, Remote Sensing, 13(21): 4282. https://doi.org/10.3390/rs13214282
Yuan, K., X. Zhuang, G. Schaefer, J. Feng, L. Guan, and H. Fang, 2021. Deep-Learning-Based Multispectral Satellite Image Segmentation for Water Body Detection, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 7422-7434. https://doi.org/10.1109/JSTARS.2021.3098678
Zhang, P., Y. Ke, Z. Zhang, M. Wang, P. Li, and S. Zhang, 2018. Urban land use and land cover classification using novel deep learning models based on high spatial resolution satellite imagery, Sensors, 18(11): 3717. https://doi.org/10.3390/s18113717
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