Open Access
Issue
ITM Web Conf.
Volume 82, 2026
International Conference on NextGen Engineering Technologies and Applications for Sustainable Development (ICNEXTS’25)
Article Number 03020
Number of page(s) 7
Section Information and Technology
DOI https://doi.org/10.1051/itmconf/20268203020
Published online 04 February 2026
  1. Y. Wan, H. Yao, J. Liu, C. Sun, A. Ma and Y. Zhong, “Low-Light and Infrared Multimodal Remote Sensing in Nighttime Rescue Mission: A Review of Anomaly Detection Methods,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1-18, 2025, Art no. 5403518, doi: 10.1109/TGRS.2025.3568456. [Google Scholar]
  2. S. Liu et al., “Hyperspectral Real-Time Online Processing Local Anomaly Detection via Multiline Multiband Progressing,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-19, 2023, Art no. 5518719, doi: 10.1109/TGRS.2023.3298790. [CrossRef] [Google Scholar]
  3. S. Liu, M. Song, B. Xue, C. -I. Chang and M. Zhang, “Hyperspectral Real-Time Local Anomaly Detection Based on Finite Markov via Line-by-Line Processing,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-20, 2024, Art no. 5503520, doi: 10.1109/TGRS.2023.3345941. [Google Scholar]
  4. J. Li, Y. Zhong, H. Zhao, Z. Gao and X. Wang, “Segmenting Remote Sensing Anomalies at Instance Level via Anomaly Map-Guided Adaptation,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-16, 2024, Art no. 5527216, doi: 10.1109/TGRS.2024.3439491. [Google Scholar]
  5. M. -T. Pham, H. Gangloff and S. Lefèvre, “Weakly Supervised Marine Animal Detection from Remote Sensing Images Using Vector-Quantized Variational Autoencoder,” IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 5559-5562, doi: 10.1109/IGARSS52108.2023.10282672. [Google Scholar]
  6. J. Li, X. Wang, S. Wang, H. Zhao and Y. Zhong, “One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-15, 2024, Art no. 5517515, doi: 10.1109/TGRS.2024.3392189. [Google Scholar]
  7. J. Li, X. Wang, H. Zhao and Y. Zhong, “Learning a Cross-Modality Anomaly Detector for Remote Sensing Imagery,” in IEEE Transactions on Image Processing, vol. 33, pp. 6607-6621, 2024, doi: 10.1109/TIP.2024.3490894. [Google Scholar]
  8. D. Liang et al., “A New Detection Method for Land Surface Anomalies From the Perspective of Thermal Infrared Remote Sensing,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1-0, 2025, Art no. 5007216, doi: 10.1109/TGRS.2025.3605164. [Google Scholar]
  9. J. Liu, M. Feng, X. Xiu, X. Zeng and J. Zhang, “Tensor Low-Rank Approximation via Plug-and- Play Priors for Anomaly Detection in Remote Sensing Images,” in IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-14, 2025, Art no. 5503014, doi: 10.1109/TIM.2025.3553235. [Google Scholar]
  10. G. Yuan et al., “Spatiotemporal Correlation Characteristics Between Thermal Infrared Remote Sensing Obtained Surface Thermal Anomalies and Reconstructed 4-D Temperature Fields of Underground Coal Fires,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-18, 2023, Art no. 4506318, doi: 10.1109/TGRS.2023.3313196. [CrossRef] [Google Scholar]
  11. M. Song, Z. Guo and H. Bao, “Spectral Similarity on Low-Rank and Sparse Component for Anomaly Detection,” 2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Helsinki, Finland, 2024, pp. 1-6, doi: 10.1109/WHISPERS65427.2024.10876453. [Google Scholar]
  12. C. -I. Chang, C. -Y. Lin, P. -C. Chung and P. F. Hu, “Iterative Spectral–Spatial Hyperspectral Anomaly Detection,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-30, 2023, Art no. 5504330, doi: 10.1109/TGRS.2023.3247660. [CrossRef] [Google Scholar]
  13. W. Jin, F. Dang and L. Zhu, “Feature Enhancement With Reverse Distillation for Hyperspectral Anomaly Detection,” in IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5, 2024, Art no. 5509705, doi: 10.1109/LGRS.2024.3456178. [Google Scholar]
  14. T. Guo, Y. Yang, L. He, C. Fu and F. Luo, “Anomaly Detection of Hyperspectral Image by Coarse-to-Fine Tensor Two-Level Decomposition,” in IEEE Geoscience and Remote Sensing Letters, vol. 22, pp. 1-5, 2025, Art no. 5501105, doi: 10.1109/LGRS.2024.3516363. [Google Scholar]
  15. X. Fu, T. Zhang, J. Cheng and S. Jia, “MMR-HAD: Multiscale Mamba Reconstruction Network for Hyperspectral Anomaly Detection,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1-14, 2025, Art no. 5516914, doi: 10.1109/TGRS.2025.3541353. [Google Scholar]
  16. C. -I. Chang, C. -Y. Lin and P. F. Hu, “Band Sampling of Hyperspectral Anomaly Detection in Effective Anomaly Space,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-29, 2024, Art no. 5502729, doi: 10.1109/TGRS.2023.3347434. [Google Scholar]
  17. M. Song, Z. Guo, L. Li, S. Liu, H. Bao and J. Li, “Global Information and Structure Tensor Guided Collaborative Representation for Anomaly Detection,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 3236-3252, 2025, doi: 10.1109/JSTARS.2024.3506116. [Google Scholar]
  18. E. Zhao, H. Zhang, N. Qu, Y. Wang and Y. Zhao, “KDAD: Knowledge Distillation-Based Anomaly Detection for Thermal Infrared Hyperspectral Image,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 26515-26529, 2025, doi: 10.1109/JSTARS.2025.3622117. [Google Scholar]
  19. X. Sun et al., “Information Entropy Estimation Based on Point-Set Topology for Hyperspectral Anomaly Detection,” IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 8209-8213, doi: 10.1109/IGARSS53475.2024.10641092. [Google Scholar]
  20. L. Li, Q. Zhang, M. Song and C. -I. Chang, “Feedback Band Group and Variation Low-Rank Sparse Model for Hyperspectral Image Anomaly Detection,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-19, 2024, Art no. 5508919, doi: 10.1109/TGRS.2024.3364573. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.