mlsanityData to 7 Oct 2026

Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications

arXiv 2301.01200 ↗First posted 20 Dec 2022cs.CV cs.AI cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

The advances in remote sensing technologies have boosted applications for Earth observation. These technologies provide multiple observations or views with different levels of information. They might contain static or temporary views with different levels of resolution, in addition to having different types and amounts of noise due to sensor calibration or deterioration. A great variety of deep learning models have been applied to fuse the information from these multiple views, known as deep multi-view or multi-modal fusion learning. However, the approaches in the literature vary greatly since different terminology is used to refer to similar concepts or different illustrations are given to similar techniques. This article gathers works on multi-view fusion for Earth observation by focusing on the common practices and approaches used in the literature. We summarize and structure insights from several different publications concentrating on unifying points and ideas. In this manuscript, we provide a harmonized terminology while at the same time mentioning the various alternative terms that are used in literature. The topics covered by the works reviewed focus on supervised learning with the use of neural network models. We hope this review, with a long list of recent references, can support future research and lead to a unified advance in the area.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 54 of its 254 references that are in the map. First posted on arXiv 20 Dec 2022.

Builds on

54 of 254 references.

References · 54

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  5. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  6. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
  7. 2016Convolutional Two-Stream Network Fusion for Video Action Recognitioncited by 384
  8. 2019What Makes Training Multi-Modal Classification Networks Hard?cited by 340
  9. 2017Gated Multimodal Units for Information Fusioncited by 244
  10. 2016Using Deep Learning for Image-Based Plant Disease Detectioncited by 216
  11. 2020More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classificationcited by 126
  12. 2020Deep Multimodal Fusion by Channel Exchangingcited by 117
38 more
  1. 2016A Survey of Multi-View Representation Learningcited by 107
  2. 2021BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrievalcited by 91
  3. 2018CentralNet: a Multilayer Approach for Multimodal Fusioncited by 86
  4. 2019MFAS: Multimodal Fusion Architecture Searchcited by 84
  5. 2022DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentationcited by 78
  6. 2022Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Reviewcited by 73
  7. 2019Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attentioncited by 71
  8. 2017Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networkscited by 64
  9. 2018Multi-Temporal Land Cover Classification with Sequential Recurrent Encoderscited by 61
  10. 2021Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Codingcited by 58
  11. 2021Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Seriescited by 49
  12. 2021Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagerycited by 44
  13. 2021Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Modelcited by 43
  14. 2016Deep Fruit Detection in Orchardscited by 38
  15. 2019A CNN-RNN Framework for Crop Yield Predictioncited by 36
  16. 2021EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction taskcited by 34
  17. 2022SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removalcited by 32
  18. 2022Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Imagescited by 30
  19. 2019Natural Disasters Detection in Social Media and Satellite imagery: a surveycited by 27
  20. 2020ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagerycited by 27
  21. 2019Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solutioncited by 26
  22. 2021Recent Advances and Trends in Multimodal Deep Learning: A Reviewcited by 26
  23. 2016Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networkscited by 25
  24. 2020Classification of Hyperspectral and LiDAR Data Using Coupled CNNscited by 25
  25. 2021Self-supervised Audiovisual Representation Learning for Remote Sensing Datacited by 24
  26. 2018Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagerycited by 22
  27. 2020Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sourcescited by 19
  28. 2020LandCoverNet: A global benchmark land cover classification training datasetcited by 18
  29. 2017Deep Multimodal Representation Learning from Temporal Datacited by 17
  30. 2019End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mappingcited by 17
  31. 2020Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learningcited by 16
  32. 2017Urban Land Cover Classification with Missing Data Modalities Using Deep Convolutional Neural Networkscited by 14
  33. 2017Using Transfer Learning for Image-Based Cassava Disease Detectioncited by 12
  34. 2019Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Datacited by 12
  35. 2021Corn Yield Prediction with Ensemble CNN-DNNcited by 12
  36. 2015Deep-Plant: Plant Identification with convolutional neural networkscited by 11
  37. 2020Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentationcited by 10
  38. 2020Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculturecited by 8

50 most cited of 54 All 54 (JSON)

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2024Impact Assessment of Missing Data in Model Predictions for Earth Observation Applicationscited by 3
  2. 2025Missing Data as Augmentation in the Earth Observation Domain: A Multi-View Learning Approachcited by 2
  3. 2024In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Datacited by 1
  4. 2025A Spatially Masked Adaptive Gated Network for multimodal post-flood water extent mapping using SAR and incomplete multispectral datacited by 1
  5. 2025Adaptive Weighted LSSVM for Multi-View Classificationcited by 0