Related references
Note: Only part of the references are listed.An automated rice mapping method based on flooding signals in synthetic aperture radar time series
Pei Zhan et al.
REMOTE SENSING OF ENVIRONMENT (2021)
AGTOC: A novel approach to winter wheat mapping by automatic generation of training samples and one-class classification on Google Earth Engine
Gaoxiang Yang et al.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2021)
Pixel-level rice planting information monitoring in Fujin City based on time-series SAR imagery
Jiatai Pang et al.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2021)
Transferable deep learning model based on the phenological matching principle for mapping crop extent
Shuang Ge et al.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2021)
Spatial domain bridge transfer : An automated paddy rice mapping method with no training data required and decreased image inputs for the large cloudy area
Chengkang Zhang et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)
A Novel Approach to the Unsupervised Extraction of Reliable Training Samples From Thematic Products
Claudia Paris et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)
Deep machine learning with Sentinel satellite data to map paddy rice production stages across West Java, Indonesia
K. R. Thorp et al.
REMOTE SENSING OF ENVIRONMENT (2021)
Towards interpreting multi-temporal deep learning models in crop mapping
Jinfan Xu et al.
REMOTE SENSING OF ENVIRONMENT (2021)
Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach
Liang Sun et al.
REMOTE SENSING OF ENVIRONMENT (2021)
Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands
Xueqin Jiang et al.
REMOTE SENSING (2021)
The 10-m crop type maps in Northeast China during 2017-2019
Nanshan You et al.
SCIENTIFIC DATA (2021)
Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model
Pengliang Wei et al.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2021)
Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics
A-Xing Zhu et al.
REMOTE SENSING (2021)
An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine
Rongguang Ni et al.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2021)
Integrating coarse-resolution images and agricultural statistics to generate sub-pixel crop type maps and reconciled area estimates
Qiong Hu et al.
REMOTE SENSING OF ENVIRONMENT (2021)
Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets
Lamin R. Mansaray et al.
GEOCARTO INTERNATIONAL (2020)
Aerial image semantic segmentation using DCNN predicted distance maps
Dengfeng Chai et al.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2020)
Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine
Luo Liu et al.
REMOTE SENSING OF ENVIRONMENT (2020)
A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks
Chunping Qiu et al.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2020)
Deep learning in environmental remote sensing: Achievements and challenges
Qiangqiang Yuan et al.
REMOTE SENSING OF ENVIRONMENT (2020)
Integration of Sentinel-1 and Sentinel-2 Data for Land Cover Mapping Using W-Net
Massimiliano Gargiulo et al.
SENSORS (2020)
Mapping Paddy Fields in Japan by Using a Sentinel-1 SAR Time Series Supplemented by Sentinel-2 Images on Google Earth Engine
Shimpei Inoue et al.
REMOTE SENSING (2020)
Mapping Rice Paddy Based on Machine Learning with Sentinel-2 Multi-Temporal Data: Model Comparison and Transferability
Weichun Zhang et al.
REMOTE SENSING (2020)
Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net
Zhuokun Pan et al.
REMOTE SENSING (2020)
A novel method for automatic potato mapping using time series of Sentinel-2 images
Davoud Ashourloo et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)
Optimal MODIS data processing for accurate multi-year paddy rice area mapping in China
Li Liu et al.
GISCIENCE & REMOTE SENSING (2020)
Pre-season crop type mapping using deep neural networks
Raghu Yaramasu et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)
A generalized approach based on convolutional neural networks for large area cropland mapping at very high resolution
Dujuan Zhang et al.
REMOTE SENSING OF ENVIRONMENT (2020)
DeepCropMapping: A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping
Jinfan Xu et al.
REMOTE SENSING OF ENVIRONMENT (2020)
Transfer Learning for Crop classification with Cropland Data Layer data (CDL) as training samples
Pengyu Hao et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2020)
Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform
Fuyou Tian et al.
REMOTE SENSING (2019)
Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks
Dengfeng Chai et al.
REMOTE SENSING OF ENVIRONMENT (2019)
Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France
Hassan Bazzi et al.
REMOTE SENSING (2019)
High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data
Mrinal Singha et al.
SCIENTIFIC DATA (2019)
Rapid Assessment of Flood Inundation and Damaged Rice Area in Red River Delta from Sentinel 1A Imagery
Anh Phan et al.
REMOTE SENSING (2019)
Multi-Temporal SAR Data Large-Scale Crop Mapping Based on U-Net Model
Sisi Wei et al.
REMOTE SENSING (2019)
Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data
Leandro Parente et al.
REMOTE SENSING (2019)
Mapping Double and Single Crop Paddy Rice With Sentinel-1A at Varying Spatial Scales and Polarizations in Hanoi, Vietnam
Kristofer Lasko et al.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2018)
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Shaoqing Ren et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)
A patch-based convolutional neural network for remote sensing image classification
Atharva Sharma et al.
NEURAL NETWORKS (2017)
Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data
Duy Ba Nguyen et al.
REMOTE SENSING LETTERS (2016)
Tracking the dynamics of paddy rice planting area in 1986-2010 through time series Landsat images and phenology-based algorithms
Jinwei Dong et al.
REMOTE SENSING OF ENVIRONMENT (2015)
Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program
Claire Boryan et al.
GEOCARTO INTERNATIONAL (2011)
A simple neural network model for the determination of aquifer parameters
N. Samani et al.
JOURNAL OF HYDROLOGY (2007)
Mapping paddy rice agriculture in southern China using multi-temporal MODIS images
XM Xiao et al.
REMOTE SENSING OF ENVIRONMENT (2005)
Share Paper
