ISPRS Journal of Photogrammetry and Remote Sensing

(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 2020-04-01 to 2024-04-01.)
ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data741
Review on Convolutional Neural Networks (CNN) in vegetation remote sensing667
Google Earth Engine for geo-big data applications: A meta-analysis and systematic review560
A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images475
Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database383
Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging239
Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark217
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery217
National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images200
A deep translation (GAN) based change detection network for optical and SAR remote sensing images194
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 samples187
Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion179
FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery178
Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks176
X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data167
Oriented objects as pairs of middle lines152
UAVid: A semantic segmentation dataset for UAV imagery143
Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine139
Self-attention for raw optical Satellite Time Series Classification138
Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model131
Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools128
PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery126
Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation121
Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery120
A Global Context-aware and Batch-independent Network for road extraction from VHR satellite imagery116
Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping115
Remote sensing image segmentation advances: A meta-analysis110
Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine109
ABCNet: Attentive bilateral contextual network for efficient semantic segmentation of Fine-Resolution remotely sensed imagery107
HyperLi-Net: A hyper-light deep learning network for high-accurate and high-speed ship detection from synthetic aperture radar imagery106
A spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications104
Deep learning-based remote and social sensing data fusion for urban region function recognition103
Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine103
Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin100
UAV in the advent of the twenties: Where we stand and what is next98
CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery95
Thick cloud and cloud shadow removal in multitemporal imagery using progressively spatio-temporal patch group deep learning95
Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images93
A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky–Golay filter92
A novel deep learning instance segmentation model for automated marine oil spill detection90
Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine89
Evaluation of Sentinel-1 & 2 time series for predicting wheat and rapeseed phenological stages89
SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search88
Automated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN)87
A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery86
Local climate zone mapping as remote sensing scene classification using deep learning: A case study of metropolitan China85
A deep learning framework for matching of SAR and optical imagery85
From LiDAR point cloud towards digital twin city: Clustering city objects based on Gestalt principles84
Under-canopy UAV laser scanning for accurate forest field measurements84
Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review83
An attention-fused network for semantic segmentation of very-high-resolution remote sensing imagery83
Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects83
Counting of grapevine berries in images via semantic segmentation using convolutional neural networks79
Robust deep alignment network with remote sensing knowledge graph for zero-shot and generalized zero-shot remote sensing image scene classification79
Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification78
Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution77
FCCDN: Feature constraint network for VHR image change detection77
Balance learning for ship detection from synthetic aperture radar remote sensing imagery76
Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion76
Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss75
Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops73
An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine71
BT-RoadNet: A boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery70
Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite data and deep learning70
Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data70
Perception and sensing for autonomous vehicles under adverse weather conditions: A survey70
ChangeMask: Deep multi-task encoder-transformer-decoder architecture for semantic change detection69
Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework67
Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters67