| A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches S Ye, RG Pontius Jr, R Rakshit ISPRS Journal of Photogrammetry and Remote Sensing 141, 137-147, 2018 | 174 | 2018 |
| A near-real-time approach for monitoring forest disturbance using Landsat time series: Stochastic continuous change detection S Ye, J Rogan, Z Zhu, JR Eastman Remote Sensing of Environment 252, 112167, 2021 | 73 | 2021 |
| Accounting for training data error in machine learning applied to earth observations A Elmes, H Alemohammad, R Avery, K Caylor, JR Eastman, L Fishgold, ... Remote Sensing 12 (6), 1034, 2020 | 72 | 2020 |
| Monitoring rubber plantation expansion using Landsat data time series and a Shapelet-based approach S Ye, J Rogan, F Sangermano ISPRS journal of photogrammetry and remote sensing 136, 134-143, 2018 | 56 | 2018 |
| Remote sensing of land change: A multifaceted perspective Z Zhu, S Qiu, S Ye Remote Sensing of Environment 282, 113266, 2022 | 55 | 2022 |
| A targeted change-detection procedure by combining change vector analysis and post-classification approach S Ye, D Chen, J Yu ISPRS Journal of Photogrammetry and Remote Sensing 114, 115-124, 2016 | 50 | 2016 |
| Detecting subtle change from dense Landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance S Ye, J Rogan, Z Zhu, TJ Hawbaker, SJ Hart, RA Andrus, AJH Meddens, ... Remote Sensing of Environment 263, 112560, 2021 | 46 | 2021 |
| Comparison of linear and nonlinear spectral unmixing approaches: a case study with multispectral TM imagery J Yu, D Chen, Y Lin, S Ye International Journal of Remote Sensing 38 (3), 773-795, 2017 | 44 | 2017 |
| Rules to write mathematics to clarify metrics such as the land use dynamic degrees RG Pontius, J Huang, W Jiang, S Khallaghi, Y Lin, J Liu, B Quan, S Ye Landscape Ecology 32, 2249-2260, 2017 | 32 | 2017 |
| An unsupervised urban change detection procedure by using luminance and saturation for multispectral remotely sensed images S Ye, D Chen Photogrammetric Engineering & Remote Sensing 81 (8), 637-645, 2015 | 26 | 2015 |
| High resolution, annual maps of field boundaries for smallholder-dominated croplands at national scales LD Estes, S Ye, L Song, B Luo, JR Eastman, Z Meng, Q Zhang, ... Frontiers in artificial intelligence 4, 744863, 2022 | 22 | 2022 |
| Object-based continuous monitoring of land disturbances from dense Landsat time series S Ye, Z Zhu, G Cao Remote Sensing of Environment 287, 113462, 2023 | 12 | 2023 |
| Tree Crown Delineation based on Peak Climbing Segmentation Algorithm N Zhang, X Zhang, S Ye Transactions of the Chinese Society for Agricultural Machinery 45 (12), 294-300, 2014 | 8* | 2014 |
| Mapping photovoltaic power stations and assessing their environmental impacts from multi-sensor datasets in Massachusetts, United States S Tao, J Rogan, S Ye, N Geron Remote Sensing Applications: Society and Environment 30, 100937, 2023 | 5 | 2023 |
| 白杨杂种三倍体小孢子母细胞减数分裂及花粉形态观察 许重阳， 王君， 叶粟， 康向阳 西北植物学报 31 (12), 2454-2458, 2011 | 4 | 2011 |
| PIE LTER Land Cover (2005), Plum Island Sound estuary, Massachusetts-Raster S Ye, RG Pontius Environmental Data Initiative, 2018 | 2 | 2018 |
| Improving maps of smallholder-dominated croplands through tight integration of human and machine intelligence LD Estes, S Ye, L Song, RB Avery, D McRitchie, R Eastman, SR Debats, ... AGU Fall Meeting Abstracts 2019, IN42A-04, 2019 | 1 | 2019 |
| Leveraging past information and machine learning to accelerate land disturbance monitoring S Ye, Z Zhu, JW Suh Remote Sensing of Environment 305, 114071, 2024 | | 2024 |
| Object-based Land Disturbance Monitoring from Landsat Time Series S Ye, Z Zhu AGU Fall Meeting Abstracts 2021, B42C-04, 2021 | | 2021 |
| Using mixed labels and a multi-stage approach to map crop types over smallholder-dominated agricultural systems L Song, B Luo, S Ye, Q Zhang, LD Estes AGU Fall Meeting Abstracts 2020, GC034-04, 2020 | | 2020 |
J. Ronald EastmanProfessor of Geography, Clark UniversityVerified email at clarku.edu
