| Prediction of arctic sea ice concentration using a fully data driven deep neural network J Chi, H Kim Remote Sensing 9 (12), 1305, 2017 | 119 | 2017 |
| Spectral unmixing-based crop residue estimation using hyperspectral remote sensing data: A case study at Purdue university J Chi, MM Crawford IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2014 | 53 | 2014 |
| Deep learning based retrieval algorithm for Arctic sea ice concentration from AMSR2 passive microwave and MODIS optical data J Chi, H Kim, S Lee, MM Crawford Remote Sensing of Environment 231, 111204, 2019 | 32 | 2019 |
| Selection of landmark points on nonlinear manifolds for spectral unmixing using local homogeneity J Chi, MM Crawford IEEE Geoscience and Remote Sensing Letters 10 (4), 711-715, 2012 | 32 | 2012 |
| Retrieval of daily sea ice thickness from AMSR2 passive microwave data using ensemble convolutional neural networks J Chi, HC Kim GIScience & Remote Sensing 58 (6), 812-830, 2021 | 19 | 2021 |
| Two-stream convolutional long-and short-term memory model using perceptual loss for sequence-to-sequence Arctic sea ice prediction J Chi, J Bae, YJ Kwon Remote Sensing 13 (17), 3413, 2021 | 15 | 2021 |
| Spectral characteristics of the Antarctic vegetation: A case study of Barton Peninsula J Chi, H Lee, SG Hong, HC Kim Remote Sensing 13 (13), 2470, 2021 | 13 | 2021 |
| Machine learning-based temporal mixture analysis of hypertemporal Antarctic sea ice data J Chi, HC Kim, SH Kang Remote Sensing Letters 7 (2), 190-199, 2016 | 13 | 2016 |
| Validation of the radiometric characteristics of Landsat 8 (LDCM) OLI Sensor using band aggregation technique of EO-1 Hyperion hyperspectral imagery J Chi Korean Journal of Remote Sensing 29 (4), 399-406, 2013 | 12 | 2013 |
| High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems JI Kim, J Chi, A Masjedi, JE Flatt, MM Crawford, AF Habib, J Lee, HC Kim Geoscience Data Journal 9 (2), 221-234, 2022 | 11 | 2022 |
| Mapping potential plant species richness over large areas with deep learning, MODIS, and species distribution models H Choe, J Chi, JH Thorne Remote Sensing 13 (13), 2490, 2021 | 11 | 2021 |
| Active landmark sampling for manifold learning based spectral unmixing J Chi, MM Crawford IEEE Geoscience and Remote Sensing Letters 11 (11), 1881-1885, 2014 | 9 | 2014 |
| An optimal image–selection algorithm for large-scale stereoscopic mapping of uav images P Lim, S Rhee, J Seo, JI Kim, J Chi, S Lee, T Kim Remote Sensing 13 (11), 2118, 2021 | 7 | 2021 |
| Research on Analytical Technique for Satellite Observstion of the Arctic Sea Ice H Kim, H Han, CU Hyun, J Chi, Y Son, S Lee Korean Journal of Remote Sensing 34 (6_2), 1283-1298, 2018 | 6 | 2018 |
| Landmark selection using homogeneity on nonlinear manifolds for unmixing hyperspectral data J Chi, MM Crawford 2012 IEEE International Geoscience and Remote Sensing Symposium, 1373-1376, 2012 | 6 | 2012 |
| Geometric and Radiometric Quality Assessments of UAV-Borne Multi-Sensor Systems: Can UAVs Replace Terrestrial Surveys? J Chi, JI Kim, S Lee, Y Jeong, HC Kim, J Lee, C Chung Drones 7 (7), 411, 2023 | 5 | 2023 |
| Sea ice type classification with optical remote sensing data J Chi, H Kim Korean Journal of Remote Sensing 34 (6_2), 1239-1249, 2018 | 3 | 2018 |
| A fully data-driven method for predicting Antarctic sea ice concentrations using temporal mixture analysis and an autoregressive model J Chi, HC Kim Remote Sensing Letters 8 (2), 106-115, 2017 | 3 | 2017 |
| Spectral unmixing-based Arctic plant species analysis using a spectral library and terrestrial hyperspectral Imagery: A case study in Adventdalen, Svalbard J Yang, YK Lee, J Chi International Journal of Applied Earth Observation and Geoinformation 125 …, 2023 | 2 | 2023 |
| Development of Web Based GIS for Polar Ocean Research JH CHI, CU HYUN, HC KIM, HM JOO, EJ YANG, HJ PARK, SH KANG Journal of the Korean Association of Geographic Information Studies 20 (1 …, 2017 | 2 | 2017 |
