Research graph
References from Cross-Year Crop and Land-Cover Classification with Limited Labels. Local targets link to admitted publications; unresolved targets remain external evidence.
Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks
10.1016/j.neunet.2018.05.019 · 2018 · External reference
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data
10.1080/15481603.2019.1650447 · 2019 · External reference
Crop mapping through a hybrid machine learning and deep learning method
2024 · External reference
Remote sensing extraction and feature analysis of abandoned farmland in hilly and mountainous areas: A case study of Xingning, Guangdong
2020 · External reference
10.3390/app12031670
10.3390/app12031670 · External reference
10.3390/rs14153806
10.3390/rs14153806 · External reference
Challenges and opportunities in remote sensing-based crop monitoring: A review
10.1093/nsr/nwac290 · 2023 · External reference
How much does multi-temporal Sentinel-2 data improve crop type classification?
2018 · External reference
A deep learning framework for crop mapping with reconstructed Sentinel-2 time series images
10.1016/j.compag.2023.108227 · 2023 · External reference
Improving crop classification accuracy with integrated Sentinel-1 and Sentinel-2 data: A case study of barley and wheat
10.1007/s41651-023-00152-2 · 2023 · External reference
10.3390/rs13132517
10.3390/rs13132517 · External reference
The role of Sentinel-2 red edge band in rice identification: A case study of Deqing county, Zhejiang province
2021 · External reference
Mapping weed infestation in maize fields using Sentinel-2 data
10.1016/j.pce.2024.103571 · 2024 · External reference
Landsat-8 vs. Sentinel-2: Examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso
10.1080/15481603.2017.1370169 · 2017 · External reference
10.3390/rs12183062
10.3390/rs12183062 · External reference
The impact of selection of reference samples and DEM on the accuracy of land cover classification based on Sentinel-2 data
2023 · External reference
10.3390/rs14040829
10.3390/rs14040829 · External reference
Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest
10.1080/10095020.2022.2100287 · 2023 · External reference
Fully Convolutional Networks for Semantic Segmentation
10.1109/tpami.2016.2572683 · 2017 · External reference
Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
10.1109/lgrs.2017.2681128 · 2017 · External reference
A comprehensive survey on weed and crop classification using machine learning and deep learning
2024 · External reference
U-Net: Convolutional networks for biomedical image segmentation
2015 · External reference
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
2018 · External reference
SE-UNet-based extraction of winter wheat planting areas
2022 · External reference
Deep learning based multi-temporal crop classification
10.1016/j.rse.2018.11.032 · 2019 · External reference
DeepCropMapping: A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping
10.1016/j.rse.2020.111946 · 2020 · External reference
An unsupervised domain adaptation deep learning method for spatial and temporal transferable crop type mapping using Sentinel-2 imagery
10.1016/j.isprsjprs.2023.04.002 · 2023 · External reference
A source-free unsupervised domain adaptation method for cross-regional and cross-time crop mapping from satellite image time series
10.1016/j.rse.2024.114385 · 2024 · External reference
10.1109/wacv56688.2023.00619
10.1109/wacv56688.2023.00619 · External reference
ChangeMamba: Remote sensing change detection with spatiotemporal state space model
2024 · External reference
ObjFormer: Learning land-cover changes from paired OSM data and optical high-resolution imagery via object-guided transformer
2024 · External reference
10.3390/rs12101620
10.3390/rs12101620 · External reference
10.3390/rs14133213
10.3390/rs14133213 · External reference
Crop planting structure identification based on Sentinel-2A data in hilly region of middle and lower reaches of Yangtze River
2021 · External reference
Assessing coastal bathymetry and climate change impacts on coastal ecosystems using Landsat 8 and Sentinel-2 satellite imagery
10.1016/j.envres.2023.117314 · 2023 · External reference
10.1109/imcet53404.2021.9665519
10.1109/imcet53404.2021.9665519 · External reference
10.3390/rs14122758
10.3390/rs14122758 · External reference
Evaluation of a deep-learning model for multispectral remote sensing of land use and crop classification
10.1016/j.cj.2022.01.009 · 2022 · External reference
PCA-based feature reduction for hyperspectral remote sensing image classification
10.1080/02564602.2020.1740615 · 2021 · External reference
Encoder-decoder with atrous separable convolution for semantic image segmentation
2018 · External reference
10.3390/rs13030454
10.3390/rs13030454 · External reference
10.3390/rs15215088
10.3390/rs15215088 · External reference
An improved semantic segmentation algorithm for high-resolution remote sensing images based on DeepLabv3
10.1038/s41598-024-60375-1 · 2024 · External reference
Unresolved reference
External reference
Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks
10.1016/j.isprsjprs.2017.05.002 · 2017 · External reference
ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data
10.1016/j.isprsjprs.2020.01.013 · 2020 · External reference
Research on the spatial temporary evolution of urban expansion in Xining city and its surrounding areas based on Landsat time series data
10.1016/j.heliyon.2024.e24846 · 2024 · External reference
10.3390/rs14143411
10.3390/rs14143411 · External reference
Identifying and warning against spatial conflicts of land use from an ecological environment perspective: A case study of the Ili River Valley, China
10.1016/j.jenvman.2023.119757 · 2024 · External reference
10.3390/rs16142553
10.3390/rs16142553 · External reference
Deep segmentation and classification of complex crops using multi-feature satellite imagery
10.1016/j.compag.2022.107249 · 2022 · External reference
Improving crop classification accuracy with integrated Sentinel-1 and Sentinel-2 data: A case study of barley and wheat
10.1007/s41651-023-00152-2 · ExternalCitation · doi-reference
Evaluation of a deep-learning model for multispectral remote sensing of land use and crop classification
10.1016/j.cj.2022.01.009 · ExternalCitation · doi-reference
Deep segmentation and classification of complex crops using multi-feature satellite imagery
10.1016/j.compag.2022.107249 · ExternalCitation · doi-reference
A deep learning framework for crop mapping with reconstructed Sentinel-2 time series images
10.1016/j.compag.2023.108227 · ExternalCitation · doi-reference
Assessing coastal bathymetry and climate change impacts on coastal ecosystems using Landsat 8 and Sentinel-2 satellite imagery
10.1016/j.envres.2023.117314 · ExternalCitation · doi-reference
Research on the spatial temporary evolution of urban expansion in Xining city and its surrounding areas based on Landsat time series data
10.1016/j.heliyon.2024.e24846 · ExternalCitation · doi-reference
Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks
10.1016/j.isprsjprs.2017.05.002 · ExternalCitation · doi-reference
ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data
10.1016/j.isprsjprs.2020.01.013 · ExternalCitation · doi-reference
An unsupervised domain adaptation deep learning method for spatial and temporal transferable crop type mapping using Sentinel-2 imagery
10.1016/j.isprsjprs.2023.04.002 · ExternalCitation · doi-reference
Identifying and warning against spatial conflicts of land use from an ecological environment perspective: A case study of the Ili River Valley, China
10.1016/j.jenvman.2023.119757 · ExternalCitation · doi-reference
Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks
10.1016/j.neunet.2018.05.019 · ExternalCitation · doi-reference
Mapping weed infestation in maize fields using Sentinel-2 data
10.1016/j.pce.2024.103571 · ExternalCitation · doi-reference
Deep learning based multi-temporal crop classification
10.1016/j.rse.2018.11.032 · ExternalCitation · doi-reference
DeepCropMapping: A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping
10.1016/j.rse.2020.111946 · ExternalCitation · doi-reference
A source-free unsupervised domain adaptation method for cross-regional and cross-time crop mapping from satellite image time series
10.1016/j.rse.2024.114385 · ExternalCitation · doi-reference
An improved semantic segmentation algorithm for high-resolution remote sensing images based on DeepLabv3
10.1038/s41598-024-60375-1 · ExternalCitation · doi-reference
PCA-based feature reduction for hyperspectral remote sensing image classification
10.1080/02564602.2020.1740615 · ExternalCitation · doi-reference
Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest
10.1080/10095020.2022.2100287 · ExternalCitation · doi-reference
Landsat-8 vs. Sentinel-2: Examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso
10.1080/15481603.2017.1370169 · ExternalCitation · doi-reference
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data
10.1080/15481603.2019.1650447 · ExternalCitation · doi-reference
Challenges and opportunities in remote sensing-based crop monitoring: A review
10.1093/nsr/nwac290 · ExternalCitation · doi-reference
10.1109/imcet53404.2021.9665519
10.1109/imcet53404.2021.9665519 · ExternalCitation · doi-reference
Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
10.1109/lgrs.2017.2681128 · ExternalCitation · doi-reference
Fully Convolutional Networks for Semantic Segmentation
10.1109/tpami.2016.2572683 · ExternalCitation · doi-reference
10.1109/wacv56688.2023.00619
10.1109/wacv56688.2023.00619 · ExternalCitation · doi-reference
10.3390/app12031670
10.3390/app12031670 · ExternalCitation · doi-reference
10.3390/rs12101620
10.3390/rs12101620 · ExternalCitation · doi-reference
10.3390/rs12183062
10.3390/rs12183062 · ExternalCitation · doi-reference
10.3390/rs13030454
10.3390/rs13030454 · ExternalCitation · doi-reference
10.3390/rs13132517
10.3390/rs13132517 · ExternalCitation · doi-reference
10.3390/rs14040829
10.3390/rs14040829 · ExternalCitation · doi-reference
10.3390/rs14122758
10.3390/rs14122758 · ExternalCitation · doi-reference
10.3390/rs14133213
10.3390/rs14133213 · ExternalCitation · doi-reference
10.3390/rs14143411
10.3390/rs14143411 · ExternalCitation · doi-reference
10.3390/rs14153806
10.3390/rs14153806 · ExternalCitation · doi-reference
10.3390/rs15215088
10.3390/rs15215088 · ExternalCitation · doi-reference
10.3390/rs16142553
10.3390/rs16142553 · ExternalCitation · doi-reference
