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Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks

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Coupling of deep learning and remote sensing: a comprehensive systematic literature review

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Multi-threaded learning control mechanism for neural networks

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Coastal Aquaculture Mapping from Very High Spatial Resolution Imagery by Combining Object-Based Neighbor Features

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Image patch-based deep learning approach for crop and weed recognition

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predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning

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Finer Resolution Mapping of Marine Aquaculture Areas Using WorldView-2 Imagery and a Hierarchical Cascade Convolutional Neural Network

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Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion

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CatBoost for RS Image Classification With Pseudo Label Support From Neighbor Patches-Based Clustering

Samat, A., Li, E., Du, P., Liu, S., Miao, Z., & Zhang, W. (2020) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.3038771 · Cited by 47 · see citing works

Urban Flood Mapping With Bitemporal Multispectral Imagery Via a Self-Supervised Learning Framework

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Advancements in satellite image classification : methodologies, techniques, approaches and applications

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A Review of Researches on Deep Learning in Remote Sensing Application

Zhu, M., He, Y., & He, Q. (2019) · International Journal of Geosciences · doi:10.4236/ijg.2019.101001 · Cited by 45 · see citing works

Optimised U-Net for Land Use–Land Cover Classification Using Aerial Photography

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Machine learning versus deep learning in land system science: a decision-making framework for effective land classification

Jane Southworth, B., Smith, A. C., Safaei, M., Rahaman, M., Alruzuq, A., Tefera, B. B., Muir, C. S., & Herrero, H. V. (2024) · Frontiers in Remote Sensing · doi:10.3389/frsen.2024.1374862 · Cited by 43 · see citing works

Detailed Land Cover Mapping from Multitemporal Landsat-8 Data of Different Cloud Cover

Karakizi, C., Karantzalos, K. G., Vakalopoulou, M., & Antoniou, G. (2018) · Remote Sensing · doi:10.3390/rs10081214 · Cited by 43 · see citing works

A Patch-Based Light Convolutional Neural Network for Land-Cover Mapping Using Landsat-8 Images

Song, H., Kim, Y., & Kim, Y. (2019) · Remote Sensing · doi:10.3390/rs11020114 · Cited by 43 · see citing works

Federated learning meets remote sensing

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Deep Extraction of Cropland Parcels from Very High-Resolution Remotely Sensed Imagery

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Mapping Impervious Surfaces in Town–Rural Transition Belts Using China’s GF-2 Imagery and Object-Based Deep CNNs

Fu, Y., Liu, K., Shen, Z., Deng, J., Gan, M., Liu, X., Lu, D., & Wang, K. (2019) · Remote Sensing · doi:10.3390/rs11030280 · Cited by 39 · see citing works

Boosting radiotherapy dose calculation accuracy with deep learning

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Semantic segmentation of high spatial resolution images with deep neural networks

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Multisource Domain Adaptation for Remote Sensing Using Deep Neural Networks

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Visual interaction networks: A novel bio-inspired computational model for image classification

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CSDBF: Dual-Branch Framework Based on Temporal–Spatial Joint Graph Attention With Complement Strategy for Hyperspectral Image Change Detection

Wang, X., Zhao, K., Zhao, X., & Li, S. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3212418 · Cited by 36 · see citing works

Enhanced contextual representation with deep neural networks for land cover classification based on remote sensing images

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Deep learning-based models for temporal satellite data processing: Classification of paddy transplanted fields

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Open-Pit Mine Extraction from Very High-Resolution Remote Sensing Images Using OM-DeepLab

Du, S., Xing, J., Li, J., Du, S., Zhang, C., & Sun, Y. (2022) · Natural Resources Research · doi:10.1007/s11053-022-10114-y · Cited by 34 · see citing works

A comparative evaluation of deep convolutional neural network and deep neural network-based land use/land cover classifications of mining regions using fused multi-sensor satellite data

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Deep Learning for Mineral and Biogenic Oil Slick Classification With Airborne Synthetic Aperture Radar Data

Laurentiis, L. D., Jones, C. E., Holt, B., Schiavon, G., & Frate, F. D. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3034722 · Cited by 33 · see citing works

Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data

Kaushik, S., Singh, T., Bhardwaj, A., Joshi, P. K., & Dietz, A. J. (2022) · Remote Sensing · doi:10.3390/rs14061352 · Cited by 33 · see citing works

Efficiency of convolutional neural networks (CNN) based image classification for monitoring construction related activities: A case study on aggregate mining for concrete production

Yeşilmen, S., & Tatar, B. (2022) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2022.e01372 · Cited by 33 · see citing works

Residual attention-based multi-scale script identification in scene text images

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Detection of Urban and Environmental Changes via Remote Sensing

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Full Convolution Neural Network Combined with Contextual Feature Representation for Cropland Extraction from High-Resolution Remote Sensing Images

Li, Z., Chen, S., Meng, X., Zhu, R., Lu, J., Cao, L., & Lu, P. (2022) · Remote Sensing · doi:10.3390/rs14092157 · Cited by 31 · see citing works

Enhanced crop classification through integrated optical and SAR data: a deep learning approach for multi-source image fusion

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Ultrasonic signal denoising based on autoencoder

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A Comprehensive Review of Land Use and Land Cover Change Based on Knowledge Graph and Bibliometric Analyses

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Design of an optimized deep learning algorithm for automatic classification of high-resolution satellite dataset (LISS IV) for studying land-use patterns in a mining region

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Large-scale agricultural greenhouse extraction for remote sensing imagery based on layout attention network: A case study of China

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Estimation of tree failure consequences due to high winds using convolutional neural networks

Kakareko, G., Jung, S., & Özgüven, E. E. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1797219 · Cited by 27 · see citing works

Multisensor Land Cover Classification With Sparsely Annotated Data Based on Convolutional Neural Networks and Self-Distillation

Gbodjo, Y. J. E., Montet, O., Ienco, D., Gaetano, R., & Dupuy, S. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3119191 · Cited by 27 · see citing works

Rice Mapping in Training Sample Shortage Regions Using a Deep Semantic Segmentation Model Trained on Pseudo-Labels

Wei, P., Huang, R., Lin, T., & Huang, J. (2022) · Remote Sensing · doi:10.3390/rs14020328 · Cited by 27 · see citing works

Innovative Hyperspectral Image Classification Approach Using Optimized CNN and ELM

Ye, A., Zhou, X., & Miao, F. (2022) · Electronics · doi:10.3390/electronics11050775 · Cited by 27 · see citing works

Computer vision models for comparing spatial patterns: understanding spatial scale

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Joint Network Combining Dual-Attention Fusion Modality and Two Specific Modalities for Land Cover Classification Using Optical and SAR Images

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Land use/land cover classification with Landsat-8 and Landsat-9 satellite images: a comparative analysis between forest- and agriculture-dominated landscapes using different machine learning methods

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Determination of land use and land cover change using multi-temporal PlanetScope images and deep learning CNN model

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Accurate extraction of offshore raft aquaculture areas based on a 3D-CNN model

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A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image Classification

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Permuted Spectral and Permuted Spectral-Spatial CNN Models for PolSAR-Multispectral Data based Land Cover Classification

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Distributed Training and Inference of Deep Learning Models for Multi-Modal Land Cover Classification

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The origin of continents and oceans

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Improving land-cover classification accuracy with a patch-based convolutional neural network: data augmentation and purposive sampling

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A land-cover classification method of high-resolution remote sensing imagery based on convolution neural network

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Fast and accurate land-cover classification on medium-resolution remote-sensing images using segmentation models

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Deep Neural Networks for Remote Sensing Image Classification

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