Publications

Detailed Information

In-Season Wall-to-Wall Crop-Type Mapping Using Ensemble of Image Segmentation Models

Cited 1 time in Web of Science Cited 1 time in Scopus
Authors

Zaheer, Sheir A.; Ryu, Youngryel; Lee, Junghee; Zhong, Zilong; Lee, Kyungdo

Issue Date
2023
Publisher
Institute of Electrical and Electronics Engineers
Citation
IEEE Transactions on Geoscience and Remote Sensing, Vol.61, p. 4411311
Abstract
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 article, we propose an ensemble of image segmentation models, based on attention U-Net architecture and optimized using focal Trvesky loss (FTL), 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 preharvest 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.
ISSN
0196-2892
URI
https://hdl.handle.net/10371/199142
DOI
https://doi.org/10.1109/TGRS.2023.3335214
Files in This Item:
There are no files associated with this item.
Appears in Collections:

Related Researcher

  • College of Agriculture and Life Sciences
  • Department of Landscape Architecture and Rural System Engineering
Research Area Biometeorology, Remote sensing, exponential technologies, 도시 생태학, 지표면 원격탐사, 탄소, 물, 에너지 순환

Altmetrics

Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Share