A temporal-spatial deep learning network for winter wheat mapping using time-series Sentinel-2 imagery-Reference-Cited by-同舟云学术

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A temporal-spatial deep learning network for winter wheat mapping using time-series Sentinel-2 imagery

Published:2024-08 Issue: Volume:214 Page:48-64
ISSN:0924-2716
Container-title:ISPRS Journal of Photogrammetry and Remote Sensing
language:en
Short-container-title:ISPRS Journal of Photogrammetry and Remote Sensing

Author:

Fan Lingling^ORCID,Xia Lang,Yang Jing,Sun Xiao,Wu Shangrong,Qiu Bingwen^ORCID,Chen Jin,Wu Wenbin,Yang Peng^ORCID

Publisher

Elsevier BV

Reference39 articles.

1. Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine;Adrian;ISPRS J. Photogramm. Remote Sens.,2021

2. Transfer learning models for land cover and land use classification in remote sensing image;Alem;Appl. Artif. Intell.,2022

3. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis;Belgiu;Remote Sens. Environ.,2018

4. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach;Cai;Remote Sens. Environ.,2018

5. Mapping Winter Wheat in North China Using Sentinel 2A/B data: a method based on phenology-time weighted dynamic time warping;Dong;Remote Sens. (Basel),2020

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