相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Optimal segmentation scale selection and evaluation of cultivated land objects based on high-resolution remote sensing images with spectral and texture features
Heng Lu et al.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2021)
Bottlenecks and opportunities in field-based high-throughput phenotyping for heat and drought stress
Nathan T. Hein et al.
JOURNAL OF EXPERIMENTAL BOTANY (2021)
Voltage losses due to the perimeter and dark area in micro-concentrator solar cells
M. Wiesenfarth et al.
SOLAR ENERGY MATERIALS AND SOLAR CELLS (2021)
Remote Sensing Image Super-Resolution Using Novel Dense-Sampling Networks
Xiaoyu Dong et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)
A deep learning approach combining instance and semantic segmentation to identify diseases and pests of coffee leaves from in-field images
Lucas M. Tassis et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)
Semantic segmentation of high-resolution satellite images using deep learning
Kuldeep Chaurasia et al.
EARTH SCIENCE INFORMATICS (2021)
Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images
Ziyi Chen et al.
REMOTE SENSING (2021)
Efficient Transformer for Remote Sensing Image Segmentation
Zhiyong Xu et al.
REMOTE SENSING (2021)
Feature Fusion Approach for Temporal Land Use Mapping in Complex Agricultural Areas
Lijun Wang et al.
REMOTE SENSING (2021)
Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning
Lifei Wei et al.
REMOTE SENSING (2021)
Recurrent Neural Networks for Time Series Forecasting: Current status and future directions
Hansika Hewamalage et al.
INTERNATIONAL JOURNAL OF FORECASTING (2021)
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data
Abdulhakim Mohamed Abdi
GISCIENCE & REMOTE SENSING (2020)
ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data
Foivos Diakogiannis et al.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2020)
Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices
Gudina Legese Feyisa et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)
UNet plus plus : Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
Zongwei Zhou et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2020)
Damage evaluation of soybean chilling injury based on Google Earth Engine (GEE) and crop modelling
Juan Cao et al.
JOURNAL OF GEOGRAPHICAL SCIENCES (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)
Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review
Michel E. D. Chaves et al.
REMOTE SENSING (2020)
Deep learning and process understanding for data-driven Earth system science
Markus Reichstein et al.
NATURE (2019)
Deep learning based multi-temporal crop classification
Liheng Zhong et al.
REMOTE SENSING OF ENVIRONMENT (2019)
Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm
Yuanhuizi He et al.
REMOTE SENSING (2019)
End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet plus
Daifeng Peng et al.
REMOTE SENSING (2019)
The Efficacy Analysis of Determining the Wooded and Shrubbed Area Based on Archival Aerial Imagery Using Texture Analysis
Przemyslaw Kupidura et al.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2019)
Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture
Dino Ienco et al.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2019)
Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
Nataliia Kussul et al.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2017)