Sheir A. Zaheer
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Sheir A. Zaheer has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Junghee Lee
Forest ICT Research Center, National Institute of Forest Science, Seoul 02455, Republic of Korea
1 shared publication - Kyung-Do Lee
Senior Researcher, National Agricultural Satellite Center, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea
1 shared publication - Youngryel Ryu
Department of Landscape Architecture and Rural Systems Engineering Seoul National University Seoul South Korea
1 shared publication - Zilong Zhong
School of Earth, Environment & Society McMaster University Hamilton ON Canada
1 shared publication
Latest Publications
- 20 November 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 61 (01962892) , 1-11
In-Season Wall-to-Wall Crop-Type Mapping Using Ensemble of Image Segmentation Models
Availability of high resolution remote sensing images along with advances in deep learning has enabled timely generation of crop type maps. However, scaling such maps to large areas is often hindered by data occlusion due to clouds. In this paper, we propose an ensemble of image segmentation models, based on attention U-Net architecture and optimized using Focal Trvesky loss, to scale crop-type mapping by tackling the missing data problem. We trained models on Landsat 8 and Sentinel 2 surface reflectance data and created a bagged ensemble of those trained models to generate wall-to-wall crop type maps of the entire US corn-belt. We also demonstrate the effectiveness of our approach in generating pre-harvest wall-to-wall crop type maps. Our proposed method obtains a competitive 92% overall accuracy for crop type mapping without requiring the availability of the entire in-season time-series of remote sensing images over the entire mapping region.
- 20 November 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 61 (01962892) , 1-11
In-Season Wall-to-Wall Crop-Type Mapping Using Ensemble of Image Segmentation Models
Availability of high resolution remote sensing images along with advances in deep learning has enabled timely generation of crop type maps. However, scaling such maps to large areas is often hindered by data occlusion due to clouds. In this paper, we propose an ensemble of image segmentation models, based on attention U-Net architecture and optimized using Focal Trvesky loss, to scale crop-type mapping by tackling the missing data problem. We trained models on Landsat 8 and Sentinel 2 surface reflectance data and created a bagged ensemble of those trained models to generate wall-to-wall crop type maps of the entire US corn-belt. We also demonstrate the effectiveness of our approach in generating pre-harvest wall-to-wall crop type maps. Our proposed method obtains a competitive 92% overall accuracy for crop type mapping without requiring the availability of the entire in-season time-series of remote sensing images over the entire mapping region.