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http://dx.doi.org/10.7780/kjrs.2022.38.6.2.1

Deep Learning for Remote Sensing Applications  

Lee, Moung-Jin (Center for Environmental Data Strategy, Korea Environment Institute)
Lee, Won-Jin (Environmental Satellite Center, National Institute of Environmental Research)
Lee, Seung-Kuk (Division of Earth and Environmental System Sciences, Pukyong National University)
Jung, Hyung-Sup (Department of Geoinformatics, University of Seoul)
Publication Information
Korean Journal of Remote Sensing / v.38, no.6_2, 2022 , pp. 1581-1587 More about this Journal
Abstract
Recently, deep learning has become more important in remote sensing data processing. Huge amounts of data for artificial intelligence (AI) has been designed and built to develop new technologies for remote sensing, and AI models have been learned by the AI training dataset. Artificial intelligence models have developed rapidly, and model accuracy is increasing accordingly. However, there are variations in the model accuracy depending on the person who trains the AI model. Eventually, experts who can train AI models well are required more and more. Moreover, the deep learning technique enables us to automate methods for remote sensing applications. Methods having the performance of less than about 60% in the past are now over 90% and entering about 100%. In this special issue, thirteen papers on how deep learning techniques are used for remote sensing applications will be introduced.
Keywords
Remote sensing; Deep learning; Artificial intelligence;
Citations & Related Records
Times Cited By KSCI : 34  (Citation Analysis)
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1 Yoon, Y.-W., H.-S. Jung, and W.-J. Lee, 2022. YOLOv5-based Chimney Detection Using High Resolution Remote Sensing Images, Korean Journal of Remote Sensing, 38(6-2): 1677-1689 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.6.2.9   DOI
2 Park, S.-H., D.-S. Kim, and J.-I. Kwon, 2022c. A Study on the GK2A/AMI Image Based Cold Water Detection Using Convolutional Neural Network, Korean Journal of Remote Sensing, 38(6-2): 1653-1661 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.6.2.7   DOI
3 Lee, S.-H. and M. Lee, 2020. A Study on Deep Learning Optimization by Land Cover Classification Item Using Satellite Imagery, Korean Journal of Remote Sensing, 36(6-2): 1591-1604 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.6.2.9   DOI
4 Lee, Y., S. Lee, J. Im, and C. Yoo, 2021. Analysis of Surface Urban Heat Island and Land Surface Temperature Using Deep Learning Based Local Climate Zone Classification: A Case Study of Suwon and Daegu, Korea, Korean Journal of Remote Sensing, 37(5-3): 1447-1460 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.5.3.9   DOI
5 Park, C.-W. and H.-S. Jung, 2022. Detection of Urban Trees using YOLOv5 from Aerial Images, Korean Journal of Remote Sensing, 38(6-2): 1633-1641 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.6.2.5   DOI
6 Park, G., J. Kang, S. Choi, Y. Youn, G. Kim, and Y. Lee, 2022a. Detection of Active Fire Objects from Drone Images Using YOLOv7x Model, Korean Journal of Remote Sensing, 38(6-2): 1737-1741 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.6.2.13   DOI
7 Park, G., Y. Youn, J. Kang, G. Kim, S. Choi, S. Jang, S. Bak, S. Gong, J. Kwak, and Y. Lee, 2022b. A Comparative Study on the Object Detection of Deposited Marine Debris (DMD) Using YOLOv5 and YOLOv7 Models, Korean Journal of Remote Sensing, 38(6-2): 1643-1652 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.6.2.6   DOI
8 Seong, S. K., H.-S. Choi, J. S. Mo, and J. Choi, 2021a. Availability Evaluation of Object Detection Based on Deep Learning Method by Using Multitemporal and Multisensor Data for Nuclear Activity Analysis, Korean Journal of Remote Sensing, 37(5-1): 1083-1094 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.5.1.20   DOI
9 Cha, S. E., H.-W. Jo, C.-H. Lim, C. Song, S.-G. Lee, J. Kim, C. Park, S. W. Jeon, and W.-K. Lee, 2020. Estimating the Stand Level Vegetation Structure Map Using Drone Optical Imageries and LiDAR Data based on an Artificial Neural Networks (ANNs), Korean Journal of Remote Sensing, 36(5-1): 653-666 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.5.1.1   DOI
10 Baek, W.-K. and H.-S. Jung, 2022. A Review on Deep-learning-based Phase Unwrapping Technique for Synthetic Aperture Radar Interferometry, Korean Journal of Remote Sensing, 38(6-2): 1589-1605 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.6.2.2   DOI
11 Cho, Y.-I., D. Yoon, and M.-J. Lee, 2022. Analysis of Spatial Correlation between Surface Temperature and Absorbed Solar Radiation Using Drone - Focusing on Cool Roof Performance, Korean Journal of Remote Sensing, 38(6-2): 1607-1622 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.6.2.3   DOI
12 Choi, Y., M. Kim, Y. Kim, and S. Han, 2020b. A Study of CNN-based Super-Resolution Method for Remote Sensing Image, Korean Journal of Remote Sensing, 36(3): 449-460 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.3.5   DOI
13 Jeon, E.-I., S.H. Kim, B.-S. Kim, K.H. Park, and O. Choi, 2020. Semantic Segmentation of Drone Imagery Using Deep Learning for Seagrass Habitat Monitoring, Korean Journal of Remote Sensing, 36(2-1): 199-215 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.2.1.8   DOI
14 Jeong, M., H. Choi, and J. Choi, 2020. Analysis of Change Detection Results by UNet++ Models According to the Characteristics of Loss Function, Korean Journal of Remote Sensing, 36(5-2): 929-937 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.5.2.7   DOI
15 Kim, J., H. Jeon, and D.-J. Kim, 2020b. Extracting Flooded Areas in Southeast Asia Using SegNet and U-Net, Korean Journal of Remote Sensing, 36(5-3): 1095-1107 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.5.3.8   DOI
16 Seong, S.K., J.S. Mo, S.-I. Na, and J. Choi. 2021b. Attention Gated FC-DenseNet for Extracting Crop Cultivation Area by Multispectral Satellite Imagery, Korean Journal of Remote Sensing, 37(5-1): 1061-1070 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.5.1.18   DOI
17 Mo, J.S., S.K. Seong, and J. Choi, 2021. Change Detection of Building Objects in Urban Area by Using Transfer Learning, Korean Journal of Remote Sensing, 37(6-1): 1685-1695 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.6.1.16   DOI
18 Kang, J., G. Kim, Y. Jeong, S. Kim, Y. Youn, S. Cho, and Y. Lee, 2021. U-Net Cloud Detection for the SPARCS Cloud Dataset from Landsat 8 Images, Korean Journal of Remote Sensing, 37(5-1): 1149-1161 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.5.1.25   DOI
19 Kang, J., G. Park, G. Kim, Y. Youn, S. Choi, and Y. Lee, 2022a. Cloud Detection from Sentinel-2 Images Using DeepLabV3+ and Swin Transformer Models, Korean Journal of Remote Sensing, 38(6-2): 1743-1747 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.6.2.14   DOI
20 Kim, E., K. Kim, S. M. Kim, T. Cui, and J.-H. Ryu, 2020a. Deep Learning Based Floating Macroalgae Classification Using Gaofen-1 WFV Images, Korean Journal of Remote Sensing, 36(2-2): 293-307 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.2.2.6   DOI
21 Kim, N., M.-S. Park, M. J. Jeong, D.-H. Hwang, and H.-J. Yoon, 2021b. A Study on Field Compost Detection by Using Unmanned Aerial Vehicle Image and Semantic Segmentation Technique based Deep Learning, Korean Journal of Remote Sensing, 37(3): 367-378 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.3.1   DOI
22 Ko, K.-S., Y.-W. Kim, S.-H. Byeon, and S.-J. Lee, 2021. LSTM Based Prediction of Ocean Mixed Layer Temperature Using Meteorological Data, Korean Journal of Remote Sensing, 37(3): 603-614 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.3.19   DOI
23 Kim, J., Y. Song, and W.-K. Lee, 2021a. Accuracy analysis of Multi-series Phenological Landcover Classification Using U-Net-based Deep Learning Model - Focusing on the Seoul, Republic of Korea, Korean Journal of Remote Sensing, 37(3): 409-418 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.3.4   DOI
24 Baek, W.-K., M.-J. Lee, and H.-S. Jung, 2022. The Performance Improvement of U-Net Model for Landcover Semantic Segmentation through Data Augmentation, Korean Journal of Remote Sensing, 38(6-2): 1663-1676 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.6.2.8   DOI
25 Choi, H., D. Seo, and J. Choi, 2020a. A Pansharpening Algorithm of KOMPSAT-3A Satellite Imagery by Using Dilated Residual Convolutional Neural Network, Korean Journal of Remote Sensing, 36(5-2): 961-973 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.5.2.10   DOI
26 Gong, S.-H., W.-K. Baek, and H.-S. Jung, 2022. Landslide Susceptibility Mapping Using Deep Neural Network and Convolutional Neural Network, Korean Journal of Remote Sensing, 38(6-2): 1723-1735 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.6.2.12   DOI
27 Song, C., W. Wahyu, J. Jung, S. Hong, D. Kim, and J. Kang, 2020. Urban Change Detection for High-resolution Satellite Images Using U-Net Based on SPADE, Korean Journal of Remote Sensing, 36(6-2): 1579-1590 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.6.2.8   DOI
28 Chung, D. and I. Lee, 2021. The Optimal GSD and Image Size for Deep Learning Semantic Segmentation Training of Drone Images of Winter Vegetables, Korean Journal of Remote Sensing, 37(6-1): 1573-1587 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.6.1.7   DOI
29 Jung, S.H., Y.J. Kim, S. Park, and J. Im, 2020. Prediction of Sea Surface Temperature and Detection of Ocean Heat Wave in the South Sea of Korea Using Time-series Deep-learning Approaches, Korean Journal of Remote Sensing, 36(5-3): 1077-1093 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.5.3.7   DOI
30 Kang, J., Y. Youn, G. Kim, G. Park, S. Choi, C.-S. Yang, J. Yi, and Y. Lee, 2022b. Detection of Marine Oil Spills from PlanetScope Images Using DeepLabV3+ Model, Korean Journal of Remote Sensing, 38(6-2): 1623-1631 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.6.2.4   DOI
31 Yu, J.W., Y.-W. Yoon, E.-R. Lee, W.-K. Baek, and H.-S. Jung, 2022. Flood Mapping Using Modified U-NET from TerraSAR-X Images, Korean Journal of Remote Sensing, 38(6-2): 1709-1722 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.6.2.11   DOI
32 Lee, E.-R., H.-S. Lee, S.-C. Park, and H.-S. Jung, 2022. Observation of Ice Gradient in Cheonji, Baekdu Mountain Using Modified U-Net from Landsat -5/-7/-8 Images, Korean Journal of Remote Sensing, 38(6-2): 1691-1707 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2022.38.6.2.10   DOI
33 Seong, S.K., S.-I. Na, and J. Choi, 2020. Assessment of the FC-DenseNet for Crop Cultivation Area Extraction by Using RapidEye Satellite Imagery, Korean Journal of Remote Sensing, 36(5-1): 823-833 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.5.1.14   DOI
34 Lee, J., C. Yoo, J. Im, Y. Shin, and D. Cho, 2020. Multitask Learning Based Tropical Cyclone Intensity Monitoring and Forecasting through Fusion of Geostationary Satellite Data and Numerical Forecasting Model Output, Korean Journal of Remote Sensing, 36(5-3): 1037-1051 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2020.36.5.3.4   DOI