ISPRS Journal of Photogrammetry and Remote Sensing

Papers
(The H4-Index of ISPRS Journal of Photogrammetry and Remote Sensing is 67. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-03-01 to 2024-03-01.)
ArticleCitations
ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data708
Review on Convolutional Neural Networks (CNN) in vegetation remote sensing632
Google Earth Engine for geo-big data applications: A meta-analysis and systematic review540
A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images446
Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database373
Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging230
Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark215
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery197
National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images194
A deep translation (GAN) based change detection network for optical and SAR remote sensing images186
Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples181
FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery175
Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion170
Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks170
Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images169
X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data164
Oriented objects as pairs of middle lines147
Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine145
UAVid: A semantic segmentation dataset for UAV imagery138
Self-attention for raw optical Satellite Time Series Classification133
Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine133
Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model129
PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery126
Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools123
Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation120
Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery112
A Global Context-aware and Batch-independent Network for road extraction from VHR satellite imagery111
Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping109
Remote sensing image segmentation advances: A meta-analysis107
Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine105
HyperLi-Net: A hyper-light deep learning network for high-accurate and high-speed ship detection from synthetic aperture radar imagery104
ABCNet: Attentive bilateral contextual network for efficient semantic segmentation of Fine-Resolution remotely sensed imagery102
Deep learning-based remote and social sensing data fusion for urban region function recognition102
A spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications101
Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine100
Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin97
UAV in the advent of the twenties: Where we stand and what is next96
Thick cloud and cloud shadow removal in multitemporal imagery using progressively spatio-temporal patch group deep learning93
Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images91
CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery90
A novel deep learning instance segmentation model for automated marine oil spill detection89
SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search88
A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky–Golay filter87
Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine87
Evaluation of Sentinel-1 & 2 time series for predicting wheat and rapeseed phenological stages87
Automated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN)85
Local climate zone mapping as remote sensing scene classification using deep learning: A case study of metropolitan China83
A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery83
Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects82
A deep learning framework for matching of SAR and optical imagery82
Under-canopy UAV laser scanning for accurate forest field measurements82
From LiDAR point cloud towards digital twin city: Clustering city objects based on Gestalt principles81
Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review80
An attention-fused network for semantic segmentation of very-high-resolution remote sensing imagery78
Robust deep alignment network with remote sensing knowledge graph for zero-shot and generalized zero-shot remote sensing image scene classification78
Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification77
Counting of grapevine berries in images via semantic segmentation using convolutional neural networks76
Accurate derivation of stem curve and volume using backpack mobile laser scanning75
Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss74
Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution71
Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion71
Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops71
Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data70
FCCDN: Feature constraint network for VHR image change detection70
Development and evaluation of a new algorithm for detecting 30 m land surface phenology from VIIRS and HLS time series69
BT-RoadNet: A boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery68
An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine68
Balance learning for ship detection from synthetic aperture radar remote sensing imagery67
The migration of training samples towards dynamic global land cover mapping67
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