EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

Deep Residual Learning for Image Recognition

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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ImageNet classification with deep convolutional neural networks

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Going deeper with convolutions

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ImageNet Large Scale Visual Recognition Challenge

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Rethinking the Inception Architecture for Computer Vision

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A Practical Guide to Splines

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Backpropagation Applied to Handwritten Zip Code Recognition

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Identity mappings in deep residual networks

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Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

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Bamboo: Building Mega-Scale Vision Dataset Continually with Human–Machine Synergy

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Fine-Tuning for Few-Shot Image Classification by Multimodal Prototype Regularization

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Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning

Elmahallawy, M., & Luo, T. (2024) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom59722.2024.10494442 · Cited by 14

Revisiting Few-Shot Learning From a Causal Perspective

Lin, G., Xu, Y., Lai, H., & Yin, J. P. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3397689 · Cited by 16

Self-supervised visual–textual prompt learning for few-shot grading of gastric intestinal metaplasia

Chen, X., Zheng, X., Li, Z., Ma, M., & Zhang, M. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112303 · Cited by 14

Tackling the Satellite Downlink Bottleneck with Federated Onboard Learning of Image Compression

Gómez, P., & Meoni, G. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00674 · Cited by 14

Design of Super Resolution and Fuzzy Deep Learning Architecture for the Classification of Land Cover and Landsliding Using Aerial Remote Sensing Data

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Context-aware prompt learning for test-time vision recognition with frozen vision-language model

Yin, J., Zhang, X., Wu, L. Y., & Wang, X. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111359 · Cited by 16

Personalized Federated Learning With State-Adaptive IoT Device Scheduling in Mobile-Edge Computing

Mai, J., Cao, K., & Wei, T. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3560221 · Cited by 14

CtxMIM: Context-Enhanced Masked Image Modeling for Remote Sensing Image Understanding

Zhang, M., Liu, Q., & Wang, Y. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3769084 · Cited by 14

High-Resolution Imagery Classification Based on Different Levels of Information

Li, E., Samat, A., Liu, W., Lin, C., & Bai, X. (2019) · Remote Sensing · doi:10.3390/rs11242916 · Cited by 14

A Novel Technique for Robust Training of Deep Networks With Multisource Weak Labeled Remote Sensing Data

Perantoni, G., & Bruzzone, L. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3091482 · Cited by 14

Three-Stream and Double Attention-Based DenseNet-BiLSTM for Fine Land Cover Classification of Complex Mining Landscapes

Zhang, D., Leng, J., Li, X., He, W., & Chen, W. (2022) · Sustainability · doi:10.3390/su141912465 · Cited by 14

Mono- and Dual-Regulated Contractive-Expansive-Contractive Deep Convolutional Networks for Classification of Multispectral Remote Sensing Images

Singh, A. K., & Bruzzone, L. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3211861 · Cited by 14

Key Algorithm for Human Motion Recognition in Virtual Reality Video Sequences Based on Hidden Markov Model

Liu, L., Jiao, Y., & Meng, F. (2020) · IEEE Access · doi:10.1109/access.2020.3020591 · Cited by 14

Learning with Enriched Inductive Biases for Vision-Language Models

Yang, L., Zhang, R., Chen, Q., & Xie, X. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02354-1 · Cited by 14

Bayesian Test-Time Adaptation for Vision-Language Models

Zhou, L., Ye, M., Li, S., Li, N., Zhu, X., Deng, L., Liu, H., & Lei, Z. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02792 · Cited by 20

Cluster-aware prompt ensemble learning for few-shot vision-language model adaptation

Chen, Z., Yu, X., Tao, X., Li, Y., & Huang, Z. H. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112596 · Cited by 14

Monitoring the Impact of Large Transport Infrastructure on Land Use and Environment Using Deep Learning and Satellite Imagery

Pavlović, M., Ilić, S., Antonić, N., & Culibrk, D. R. (2022) · Remote Sensing · doi:10.3390/rs14102494 · Cited by 13

Extent of Lake Tana’s Papyrus Swamps (1985–2020), North Ethiopia

Kahsay, A., Demissie, B., Nyssen, J., Triest, L., Lemmens, P., Meester, L. D., Kibret, M., Verleyen, E., Adgo, E., & Stiers, I. (2022) · Wetlands · doi:10.1007/s13157-022-01651-6 · Cited by 13

Short to medium range impact based forecasting of heavy rainfall in India

Mohapatra, M., Chauhan, A., Varshney, A., Gurjar, S., Bushair, M. T., Sharma, M., JENAMANI, R. K., Srivastava, K., Guhathakurta, P., Chattopadhyay, R., et al. (2023) · MAUSAM · doi:10.54302/mausam.v74i2.6180 · Cited by 13

POPNASv3: A pareto-optimal neural architecture search solution for image and time series classification

Falanti, A., Lomurno, E., Ardagna, D., & Matteucci, M. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110555 · Cited by 13

Mini but Mighty: Finetuning ViTs with Mini Adapters

Marouf, I. E., Tartaglione, E., & Lathuilière, S. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00175 · Cited by 13

Spatial data intelligence and city metaverse: A review

Meng, X., Li, Y., Liu, K., Liu, Y., Yang, B., Song, X., Liao, G., Wang, S., Yu, Z., Chen, L., et al. (2023) · Fundamental Research · doi:10.1016/j.fmre.2023.10.014 · Cited by 19

Compound Text-Guided Prompt Tuning via Image-Adaptive Cues

Tan, H., Li, J., Zhou, Y., Wan, J., Lei, Z., & Zhang, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28311 · Cited by 15

Quantifying the scale of erosion along major coastal aquifers of Pakistan using geospatial and machine learning approaches

Aeman, H., Shu, H., Aisha, H., Nadeem, I., & Aslam, R. W. (2024) · Environmental Science and Pollution Research · doi:10.1007/s11356-024-33296-9 · Cited by 13

Cross-Modal Contrastive Learning With Spatiotemporal Context for Correlation-Aware Multiscale Remote Sensing Image Retrieval

Zhu, L., Wang, Y., Hu, Y., Su, X., & Fu, K. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3417421 · Cited by 14

Adapting LoRa Ground Stations for Low-latency Imaging and Inference from LoRa-enabled CubeSats

Gadre, A., Machester, Z., & Kumar, S. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3675170 · Cited by 13

Self-Supervised Learning on Small In-Domain Datasets Can Overcome Supervised Learning in Remote Sensing

Sanchez‐Fernandez, A. J., Moreno‐Álvarez, S., Rico‐Gallego, J., & Tabik, S. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3421622 · Cited by 13

Spectral Decomposition and Transformation for Cross-domain Few-shot Learning

Liu, Y., Zou, Y., Li, R., & Li, Y. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106536 · Cited by 13

Enhancing Few-Shot CLIP With Semantic-Aware Fine-Tuning

Zhu, Y. T., Chen, Y., Mao, X., Yan, X. J., Wang, Y., Wang, L., Wang, J., & Ji, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3443394 · Cited by 15

Label Propagation for Zero-shot Classification with Vision-Language Models

Stojnić, V., Kalantidis, Y., & Tolias, G. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02190 · Cited by 14

Adapting Vision-Language Models via Learning to Inject Knowledge

Xuan, S., Yang, M., & Zhang, S. S. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3468884 · Cited by 15

Self-adapting Large Visual-Language Models to Edge Devices Across Visual Modalities

Cai, K., Duan, Z., Liu, G., Fleming, C., & Lu, C. X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73390-1_18 · Cited by 13

CLIP-DPO: Vision-Language Models as a Source of Preference for Fixing Hallucinations in LVLMs

Ouali, Y., Bulat, A., Martínez, B., & Tzimiropoulos, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73116-7_23 · Cited by 23

BadMerging: Backdoor Attacks Against Model Merging

Zhang, J., Chi, J., Li, Z., Cai, K., Zhang, Y., & Tian, Y. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690284 · Cited by 26

Consistent prompt learning for vision-language models

Zhang, Y., & Tian, X. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.112974 · Cited by 15

ALANINE: A Novel Decentralized Personalized Federated Learning for Heterogeneous LEO Satellite Constellation

Zhao, L., Geng, S., Tang, X., Hawbani, A., Sun, Y., Xu, L., & Tarchi, D. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3545429 · Cited by 13

Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models

Sui, E., Wang, X., & Yeung, S. Y. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00090 · Cited by 24

Fully fine-tuned CLIP models are efficient few-shot learners

Liu, M., Li, B., Dan, J., Lu, Z., Wang, Z., & Yu, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113819 · Cited by 13

Quantum computing for space applications: a selective review and perspectives

Torta, P., Casati, R., Bruni, S., Mandarino, A., & Prati, E. (2025) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-025-00369-8 · Cited by 16

Deep Learning Innovations: ResNet Applied to SAR and Sentinel-2 Imagery

Bilotta, G., Bibbò, L., Meduri, G. M., Genovese, E., & Barrile, V. (2025) · Remote Sensing · doi:10.3390/rs17121961 · Cited by 13

Unsupervised deep learning based change detection in Sentinel-2 images

Saha, S., Solano‐Correa, Y. T., Bovolo, F., & Bruzzone, L. (2019) · International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) · doi:10.1109/multi-temp.2019.8866899 · Cited by 13

A Residual-Dyad Encoder Discriminator Network for Remote Sensing Image Matching

Khurshid, N., Tharani, M., Taj, M., & Qureshi, F. Z. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2951820 · Cited by 14

LULC Image Classification with Convolutional Neural Network

Balarabe, A. T., & Jordanov, I. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9555015 · Cited by 14

Spectral Indexes Evaluation for Satellite Images Classification using CNN

Yaloveha, V., Hlavcheva, D., & Podorozhniak, A. (2021) · Journal of information and organizational sciences · doi:10.31341/jios.45.2.5 · Cited by 13

ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning

Oh, J., Kim, S., Ho, N., Kim, J.-H., Song, H., & Yun, S.-Y. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557681 · Cited by 14

Cross-domain few-shot learning based on pseudo-Siamese neural network

Gong, Y., Yue, Y., Ji, W., & Zhou, G. (2023) · Scientific Reports · doi:10.1038/s41598-023-28588-y · Cited by 13

Cross-Domain Few-Shot Classification via Dense-Sparse-Dense Regularization

Ji, F., Chen, Y., Liu, L., & Yuan, X. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3294332 · Cited by 14

Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019

Liu, Z., Pavão, A., Xu, Z., Escalera, S., Ferreira, F., Guyon, I., Hong, S., Hutter, F., Ji, R., Jacques, J. C. S., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3075372 · Cited by 17

Transfer Learning with Res2Net for Remote Sensing Scene Classification

Das, A., & Chandran, S. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377148 · Cited by 13

Multi-Modal Self-Supervised Representation Learning for Earth Observation

Jain, P., Schoen-Phelan, B., & Ross, R. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9553741 · Cited by 13

Land Cover Change Detection via Semantic SegmentationPreprint

Su, R., & Chen, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.12903 · Cited by 13

Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves

Wu, S., Zhang, J., Zeng, P., Gao, L., Song, J., & Shen, H. T. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01372 · Cited by 15

KAID: Knowledge-Aware Interactive Distillation for Vision-Language Models

Zhang, D., Wang, F., Li, B., Zhao, Z., Gao, J., & Li, X. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755008 · Cited by 17

Remote sensing scene image classification model based on multi-scale features and attention mechanism

Wang, G., Xu, H., Wang, X., Yuan, L., & Wen, X. (2022) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.16.044510 · Cited by 12

DiffDis: Empowering Generative Diffusion Model with Cross-Modal Discrimination Capability

Huang, R., Han, J., Lu, G., Liang, X., Zeng, Y., Zhang, W., & Xu, H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01440 · Cited by 15

A Deep Convolutional Spiking Neural Network for Embedded Applications

Javanshir, A., Nguyen, T. T., Mahmud, M. A. P., & Kouzani, A. Z. (2022) · Progress in Artificial Intelligence · doi:10.1007/s13748-024-00313-4 · Cited by 13

Ensemble Machine Learning on the Fusion of Sentinel Time Series Imagery with High-Resolution Orthoimagery for Improved Land Use/Land Cover Mapping

Subedi, M. R., Portillo‐Quintero, C., McIntyre, N. E., Kahl, S. S., Cox, R. D., Perry, G., & Song, X. (2024) · Remote Sensing · doi:10.3390/rs16152778 · Cited by 12

Cluster prototype earth mover’s distance adapters and alignment-guided prompt learning for vision–language models

Dong, M., Li, F., Li, Z., & Li, Z. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110861 · Cited by 12

Low-Resource Vision Challenges for Foundation Models

Zhang, Y., Doughty, H., & Snoek, C. G. M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02073 · Cited by 16

Any-Shift Prompting for Generalization Over Distributions

Xiao, Z., Shen, J., Derakhshani, M. M., Liao, S., & Snoek, C. G. M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01314 · Cited by 20

FedOrbit: Energy Efficient Federated Learning for Orbital Edge Computing Using Block Minifloat Arithmetic

Jabbarpour, M. R., Javadi, B., Leong, P. H. W., Calheiros, R. N., & Boland, D. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3478768 · Cited by 12

Improving Zero-Shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation

Mistretta, M., Baldrati, A., Bertini, M., & Bagdanov, A. D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72907-2_27 · Cited by 16

Industrial Language-Image Dataset (ILID): Adapting Vision Foundation Models for Industrial Settings

Moenck, K., Thieu, D. T., Koch, J., & Schüppstuhl, T. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.10.084 · Cited by 13

CTPT: Continual Test-time Prompt Tuning for vision-language models

Wang, F., Han, Z., Liu, X., Yin, Y., & Gao, X. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111300 · Cited by 12

An empirical evaluation of deep semi-supervised learning

Fredriksson, T., Bosch, J., & Olsson, H. H. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00713-8 · Cited by 12

A Novel Approach for High-Resolution Coastal Areas and Land Use Recognition From Remote Sensing Images Based on Multimodal Network-Level Fusion of SRAN3 and Lightweight Four Encoders ViT

Bhatti, M. K., Khan, M. A., Shaheen, S., Hamza, A., Arishi, A., AlHammadi, D. A., Algamdi, S. A., & Nam, Y. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3542194 · Cited by 12

DC-CLIP: Multilingual CLIP Compression via vision-language distillation and vision-language alignment

Zhang, W., Zhang, Y., Lin, J., Huang, B., Zhang, J., & Yu, W. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111547 · Cited by 13

On the disagreement problem in Human-in-the-Loop federated machine learning

Huelser, M. J. M., Mueller, H., Díaz-Rodríguez, N., & Holzinger, A. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100827 · Cited by 12

SenCLIP: Enhancing Zero-Shot Land-Use Mapping for Sentinel-2 with Ground-Level Prompting

Jain, P., Ienco, D., Interdonato, R., Berchoux, T., & Marcos, D. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00552 · Cited by 17

The influence of correlated features on neural network attribution methods in geoscience

Krell, E., Mamalakis, A., King, S. A., Tissot, P. E., & Ebert‐Uphoff, I. (2025) · Environmental Data Science · doi:10.1017/eds.2025.19 · Cited by 12

Optical Remote Sensing for Global Flood Disaster Mapping: A Critical Review Towards Operational Readiness

Zhang, M., Chen, Z., Wang, J., Kar, B., Pierce, M., Tiampo, K. F., Eguchi, R. T., & Glasscoe, M. T. (2025) · Remote Sensing · doi:10.3390/rs17111886 · Cited by 12

IDEA: Image description enhanced CLIP-adapter for image classification

Ye, Z., Jiang, F., Wang, Q., Huang, K., & Huang, J. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112224 · Cited by 17

Advancing agricultural remote sensing: A comprehensive review of deep supervised and Self-Supervised Learning for crop monitoring

da Silva, M. P., Correa, S. P. L. P., Schaefer, M. A. R., Reis, J. C. S., Nunes, I. M., dos Santos, J. A., & Oliveira, H. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104434 · Cited by 15

RAR: Retrieving and Ranking Augmented MLLMs for Visual Recognition

Liu, Z., Sun, Z., Zang, Y., Li, W., Zhang, P., Dong, X., Xiong, Y., Lin, D., & Wang, J. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3644175 · Cited by 28

Ensemble Classification Technique for Water Detection in Satellite Images

Jony, R. I., Woodley, A., Aishvarya, R., & Perrin, D. (2018) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2018.8615870 · Cited by 12

Multispectral Data Analysis for Semantic Assessment—A SNAP Framework for Sentinel-2 Use Case Scenarios

Grivei, A.-C., Neagoe, I. C., Georgescu, F.-A., Griparis, A., Văduva, C., Bartalis, Z., & Datcu, M. P. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3013091 · Cited by 12

Semi-Supervised Multi-Spectral Land Cover Classification With Multi-Attention and Adaptive Kernel

Zhang, K., & Yang, H. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190850 · Cited by 12

Urbanization effect of mega sporting events using sentinel-2 satellite images: The case of the pyeongchang olympics

Do, J., Ahn, S., & Kang, J. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103158 · Cited by 12

Evaluation of Riparian Tree Cover and Shading in the Chauga River Watershed Using LiDAR and Deep Learning Land Cover Classification

Bolick, M. M., Post, C. J., Mikhailova, E. A., Zurqani, H. A., Grunwald, A. P., & Saldo, E. A. (2021) · Remote Sensing · doi:10.3390/rs13204172 · Cited by 12

Energy-Based Models in Earth Observation: From Generation to Semisupervised Learning

Castillo-Navarro, J., Saux, B. L., Boulch, A., & Lefèvre, S. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3126428 · Cited by 12

Automatic generation of land use maps using aerial orthoimages and building floor data with a Conv-Depth Block (CDB) ResU-Net architecture

Yoo, S., Lee, J., Farkoushi, M. G., Lee, E., & Sohn, H. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102678 · Cited by 12

A Lightweight Model of VGG-U-Net for Remote Sensing Image Classification

Ye, M., Li, J., Luo, T., Li, S., Zhang, T., Feng, R., Hu, T., Gong, H., Ying, G., Sun, Y., et al. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.026880 · Cited by 12

End-to-End Convolutional Neural Network Feature Extraction for Remote Sensed Images Classification

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DATA: Domain-Aware and Task-Aware Self-supervised Learning

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