Research graph
References from Land-use classification based on high-resolution remote sensing imagery and deep learning models. Local targets link to admitted publications; unresolved targets remain external evidence.
Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning
10.1016/j.rse.2016.09.007 · 2016 · External reference
Mapping Pervious Surfaces and Canopy Cover Using High-Resolution Airborne Imagery and Digital Elevation Models to Support Urban Planning
10.3390/su14106149 · 2022 · External reference
Separating grazing and rainfall effects at regional scale using remote sensing imagery: A dynamic reference-cover method
10.1016/j.rse.2012.02.021 · 2012 · External reference
Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America
10.1016/j.isprsjprs.2021.06.010 · 2021 · External reference
Characterization and crop planning of rabi fallows using remote sensing and GIS
2015 · External reference
Automation of land use planning based on geoinformation modeling. In Proceedings of the IOP Conference Series
2021 · External reference
Land-Cover Change And Environmental Impact Analysis In The Greater Mankato Area Of Minnesota Using Remote Sensing And Gis Modelling
10.1080/01431160701294703 · 2008 · External reference
A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification
10.1016/j.isprsjprs.2017.07.014 · 2018 · External reference
Integrating multitemporal Sentinel-1/2 data for coastal land cover classification using a multibranch convolutional neural network: A case of the Yellow River Delta
10.3390/rs11091006 · 2019 · External reference
A Deep-Local-Global Feature Fusion Framework for High Spatial Resolution Imagery Scene Classification
10.3390/rs10040568 · 2018 · External reference
An assessment of support vector machines for land cover classification
10.1080/01431160110040323 · 2002 · External reference
Support vector machines for classification in remote sensing
10.1080/01431160512331314083 · 2005 · External reference
Random forest classifier for remote sensing classification
10.1080/01431160412331269698 · 2005 · External reference
Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms
10.1016/j.jag.2009.11.002 · 2010 · External reference
A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery
10.1016/j.rse.2011.11.020 · 2012 · External reference
Multi-resolution segmentation for object-based classification and accuracy assessment of land use/land cover classification using remotely sensed data
10.1007/s12524-008-0020-4 · 2008 · External reference
Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification. Forest Ecology &
2003 · External reference
Object-based crop identification using multiple vegetation indices, textural features and crop phenology
10.1016/j.rse.2011.01.009 · 2011 · External reference
Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China
10.1080/01431160600702632 · 2006 · External reference
Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery
10.1016/j.rse.2010.12.017 · 2013 · External reference
Supervised methods of image segmentation accuracy assessment in land cover mapping
10.1016/j.rse.2017.11.024 · 2018 · External reference
Deep Cube-Pair Network for Hyperspectral Imagery Classification
10.3390/rs10050783 · 2018 · External reference
Fully Convolutional Instance-Aware Semantic Segmentation
2016 · External reference
Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification.
10.3390/rs9101042 · 2017 · External reference
Hyperspectral and Multispectral Image Fusion via Deep Two-Branches Convolutional Neural Network
10.3390/rs10050800 · 2018 · External reference
Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network
10.3390/rs9111139 · 2017 · External reference
Spectral–spatial classification of hyperspectral images using deep convolutional neural networks.
10.1080/2150704x.2015.1047045 · 2015 · External reference
Explainable deep convolutional neural networks for insect pest recognition
10.1016/j.jclepro.2022.133638 · 2022 · External reference
Vehicle detection in remote sensing images using denoizing-based convolutional neural networks.
10.1080/2150704x.2016.1258127 · 2017 · External reference
Automatic Ship Classification from Optical Aerial Images with Convolutional Neural Networks.
10.3390/rs10040511 · 2018 · External reference
Geospatial Object Detection in Remote Sensing Imagery Based on Multiscale Single-Shot Detector with Activated Semantics.
10.3390/rs10060820 · 2018 · External reference
Domain Adaptation via a Task-Specific Classifier Framework for Remote Sensing Cross-Scene Classification
10.1109/tgrs.2022.3151689 · 2022 · External reference
Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery.
10.3390/rs71114680 · 2015 · External reference
SatCNN: satellite image dataset classification using agile convolutional neural networks
10.1080/2150704x.2016.1235299 · 2017 · External reference
EMMCNN: An ETPS-based multi-scale and multi-feature method using CNN for high spatial resolution image land-cover classification.
10.3390/rs12010066 · 2019 · External reference
Fully convolutional networks for semantic segmentation
2015 · External reference
Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network.
10.3390/rs9050498 · 2017 · External reference
Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection. Isprs Journal of Photogrammetry &
2016 · External reference
Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks. Isprs Journal of Photogrammetry &
2017 · External reference
Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping.
10.1016/j.rse.2019.111253 · 2019 · External reference
A cloud detection algorithm for satellite imagery based on deep learning.
10.1016/j.rse.2019.03.039 · 2019 · External reference
Multi-category segmentation of Sentinel-2 images based on the Swin UNet method[J].
10.3390/rs14143382 · 2022 · External reference
Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks.
10.1016/j.isprsjprs.2017.11.011 · 2018 · External reference
Segnet: A deep convolutional encoder-decoder architecture for image segmentation
10.1109/tpami.2016.2644615 · 2017 · External reference
10.1007/978-3-319-24574-4_28
10.1007/978-3-319-24574-4_28 · External reference
Swin-unet: Unet-like pure transformer for medical image segmentation.
2022 · External reference
10.1007/978-3-030-31654-9_52
10.1007/978-3-030-31654-9_52 · External reference
Unresolved reference
External reference
Adaptive contourlet fusion clustering for SAR image change detection
10.1109/tip.2022.3154922 · 2022 · External reference
SWTRU: star-shaped window transformer reinforced U-net for medical image segmentation
10.1016/j.compbiomed.2022.105954 · 2022 · External reference
A combined loss-based multiscale fully convolutional network for high-resolution remote sensing image change detection
10.1109/lgrs.2022.3216627 · 2021 · External reference
Deep learning for image super-resolution: A survey
10.1109/tpami.2020.2982166 · 2020 · External reference
Multi-scale attention integrated hierarchical networks for high-resolution building footprint extraction
10.1016/j.jag.2022.102768 · 2022 · External reference
Construction and application of a knowledge graph.
10.3390/rs13132511 · 2021 · External reference
