journal article·2024Open access

Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery

Haotang Tan, Song Sun, Tian Cheng, Xiyuan Shu

Computers, Materials & Continua· Volume 80· Issue 1

Abstract

Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmental monitoring. Addressing the limitations of conventional convolutional neural networks, we propose an in... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Remote-Sensing Image Classification · Remote Sensing and Land Use

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (53)

Show source-provided references
1. Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data
2. Status of land cover classification accuracy assessment10.1016/S0034-4257(01)00295-4 ↗
3. Developments in landsat land cover classification methods: A review10.3390/rs9090967 ↗
4. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs10.3390/rs5084124 ↗
5. Remote sensing in agriculture—accomplishments, limitations, and opportunities10.3390/rs12223783 ↗
6. Remote sensing in urban planning: Contributions towards ecologically sound policies?10.1016/j.landurbplan.2020.103921 ↗
7. A hybrid thresholding algorithm for cloud detection on ground-based color images10.1175/JTECH-D-11-00009.1 ↗
8. Estimating fractional snow cover from MODIS using the normalized difference snow index10.1016/j.rse.2003.10.016 ↗
9. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing10.1007/s11676-020-01155-1 ↗
10. Boosting night-time scene parsing with learnable frequency10.1109/TIP.2023.3267044 ↗
11. Night-time scene parsing with a large real dataset10.1109/TIP.2021.3122004 ↗
12. Solving multi-objective optimization problem of convolutional neural network using fast forward quantum optimization algorithm: Application in digital image classification10.1016/j.advengsoft.2022.103370 ↗
13. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
14. CSFwinformer: Cross-space-frequency window transformer for mirror detection10.1109/TIP.2024.3372468 ↗
15. Cloud detection algorithm comparison and validation for operational Landsat data products10.1016/j.rse.2017.03.026 ↗
16. Automated detection of cloud and cloud shadow in single-date Landsat imagery using neural networks and spatial post-processing10.3390/rs6064907 ↗
17. Cloud-Net: An end-to-end cloud detection algorithm for Landsat 8 imagery
18. Ready-to-use methods for the detection of clouds, cirrus, snow, shadow, water and clear sky pixels in Sentinel-2 MSI images10.3390/rs8080666 ↗
19. Baetens · Sentinel-2 Reference Cloud Masks Generated by an Active Learning Method · 2018
20. Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery10.1016/j.rse.2017.01.026 ↗
21. DABNet: Deformable contextual and boundary-weighted network for cloud detection in remote sensing images
22. Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors10.1016/j.isprsjprs.2019.02.017 ↗
23. Cloud and cloud shadow detection for Landsat images: The fundamental basis for analyzing Landsat10.1201/9781315166636-1 ↗
24. Cloud and cloud shadow detection for optical satellite imagery: Features, algorithms, validation, and prospects10.1016/j.isprsjprs.2022.03.020 ↗
25. Cloud masking for ocean color data processing in the coastal regions10.1109/TGRS.2006.876293 ↗
26. MSG/SEVIRI cloud mask and type from SAFNWC10.1080/01431160500166128 ↗
27. Object-based cloud and cloud shadow detection in Landsat imagery
28. Radiometric normalization and cloud detection of optical satellite images using invariant pixels10.1016/j.isprsjprs.2015.05.003 ↗
29. Cloud and snow discrimination for CCD images of HJ-1A/B constellation based on spectral signature and spatio-temporal context10.3390/rs8010031 ↗
30. Introducing two random forest based methods for cloud detection in remote sensing images10.1016/j.asr.2018.04.030 ↗
31. A cloud image detection method based on SVM vector machine10.1016/j.neucom.2014.09.102 ↗
32. Convolutional neural networks for multispectral image cloud masking
33. Coupling of deep learning and remote sensing: A comprehensive systematic literature review10.1080/01431161.2022.2161856 ↗
34. Positive-negative receptive field reasoning for omni-supervised 3D segmentation10.1109/TPAMI.2023.3319470 ↗
35. Image understands point cloud: Weakly supervised 3D semantic segmentation via association learning10.1109/TIP.2024.3372449 ↗
36. PromptAD: Learning prompts with only normal samples for few-shot anomaly detection
37. Uncertainty representation using fuzzy-entropy approach: Special application in remotely sensed high-resolution satellite images (RSHRSIs)10.1016/j.asoc.2018.07.038 ↗
38. Boosting semi-supervised learning by exploiting all unlabeled data
39. Mask guided diverse face image synthesis
40. A survey of visual transformers
41. J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
42. RoBERTa: A robustly optimized BERT pretraining approach
43. Language models are few-shot learners
44. A. Dosovitskiy et al., “An image is worth 16 × 16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020.
45. End-to-end object detection with transformers
46. Learning transferable visual models from natural language supervision
47. Learning task-aware language-image representation for class-incremental object detection
48. Edge-aware graph representation learning and reasoning for face parsing
49. I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv: 1711.05101, 2017.
50. Res2Net: A new multi-scale backbone architecture10.1109/TPAMI.2019.2938758 ↗
51. Deep residual learning for image recognition
52. Aggregated residual transformations for deep neural networks
53. L. C. Chen, G. Papandreou, F. Schroff, and H. Adam, “Rethinking atrous convolution for semantic image segmentation,” arXiv preprint arXiv:1706.05587, 2017.