Underwater Target Recognition via Cayley-Klein Measure and Shape Prior Information in Hyperspectral Imaging
Source abstract
Abstract
Underwater target detection plays a vital role in various application scenarios, ranging from scientific research to military and industrial operations. In this paper, a detection method via the Cayley–Klein measure and a prior information of shape is proposed for the issue of hyperspectral underwater target identification. Firstly, by analyzing the data features of underwater targets and backgrounds, a background suppression algorithm based on Cayley–Klein measure is developed to enhance the differentiation between underwater targets and backgrounds. Then, a local peak-based algorithm is designed to discriminate potential underwater target points based on the local peak features of underwater targets. Finally, pseudo-target points are eliminated based on the priori shape information of underwater targets. Experiments show that the algorithm proposed is efficient and can effectively detect underwater targets from hyperspectral images.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote-Sensing Image Classification · Image and Signal Denoising Methods · Advanced Image Fusion Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Aviation Operations and Service Institute, Naval Aviation University, Yantai 264000, China
Cost Defence College, Naval Aviation University, Yantai 264000, China
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Funding
National Natural Science Foundation of China
References (36)
Show source-provided references
1. Advance and Future Challenges in Hyperspectral Target Detection
2. Hyperspectral target detection: An overview of current and future challenges10.1109/MSP.2013.2278992 ↗
3. Kernel matched subspace detectors for hyperspectral target detection10.1109/TPAMI.2006.39 ↗
4. Target dictionary construction-based sparse representation hyperspectral target detection methods10.1109/JSTARS.2019.2902430 ↗
5. Hyperspectral Subpixel Target Detection Based on Interaction Subspace Model10.1016/j.patcog.2023.109464 ↗
6. Graph-Embedding Balanced Transfer Subspace Learning for Hyperspectral Cross-Scene Classification10.1109/JSTARS.2022.3163423 ↗
7. Hu, X., Xie, C., Fan, Z., Duan, Q., Zhang, D., Jiang, L., Wei, X., Hong, D., Li, G., and Zeng, X. (2022). Hyperspectral anomaly detection using deep learning: A review. Remote Sens., 14.10.3390/rs14091973 ↗
8. Spectral-spatial deep support vector data description for hyperspectral anomaly detection
9. Hyperspectral anomaly detection with relaxed collaborative representation
10. Deep Learning for Hyperspectral Imagery Classification: The State of the Art and Prospects
11. A survey: Deep learning for hyperspectral image classification with few labeled samples10.1016/j.neucom.2021.03.035 ↗
12. Hyperspectral remote sensing for shallow waters. ii. deriving bottom depths and water properties by optimization10.1364/AO.38.003831 ↗
13. Remote bathymetry of the littoral zone from A VIRIS, LASH and QuickBird imagery10.1109/TGRS.2004.841246 ↗
14. Retrieval of water quality from airborne imaging spectrometry of various lake types in different seasons10.1016/S0048-9697(00)00685-9 ↗
15. Feasibility of hyperspectral remote sensing for mapping benthic macroalgal cover in turbid coastal waters—A baltic sea case study
16. An Underwater Target Detection Framework for Hyperspectral Imagery10.1109/JSTARS.2020.2969013 ↗
17. The Mahalanobis distance10.1016/S0169-7439(99)00047-7 ↗
18. Projective spaces with Cayley-Klein metrics10.1007/s00022-004-1679-5 ↗
19. Bi, Y., Fan, B., and Wu, F. (2015, January 7–12). Beyond Mahalanobis metric: Cayley-Klein metric learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.
20. Bi, Y., Fan, B., and Wu, F. (2017). Multiple Cayley-Klein metric learning. PLoS ONE, 12.10.1371/journal.pone.0184865 ↗
21. Mika, S., Ratsch, G., Weston, J., Scholkopf, B., and Mullers, K.-R. (1999, January 25). Fisher discriminant analysis with kernels. Neural Networks for Signal Processing IX. Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No. 98th8468), Madison, WI, USA.
22. A SimRank based ensemble method for resolving challenges of partition clustering methods
23. Density-based clustering10.1002/widm.30 ↗
24. Liu, Y., Tu, W., Zhou, S., Liu, X., Song, L., Yang, X., and Zhu, E. (March, January 22). Deep graph clustering via dual correlation reduction. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual.
25. Adaptive grid-based forest-like clustering algorithm10.1016/j.neucom.2022.01.089 ↗
26. Improving projected fuzzy K-means clustering via robust learning10.1016/j.neucom.2022.03.043 ↗
27. Online structural clustering based on DBSCAN extension with granular descriptors10.1016/j.ins.2022.06.027 ↗
28. Clustering by fast search and find of density peaks10.1126/science.1242072 ↗
29. Clustering by local gravitation10.1109/TCYB.2017.2695218 ↗
30. Campbell, J.B., and Wynne, R.H. (2011). Introduction to Remote Sensing, Guilford Press.
31. Hyperspectral anomaly detection with kernel isolation forest10.1109/TGRS.2019.2936308 ↗
32. Visual attention and background subtraction with adaptive weight for hyperspectral anomaly detection10.1109/JSTARS.2021.3052968 ↗
33. A hyperspectral anomaly detection algorithm using sub-features grouping and binary accumulation10.1109/LGRS.2022.3156057 ↗
34. Hou, Z., Cheng, S., and Hu, T. (2022). A spectral-spatial fusion anomaly detection method for hyperspectral imagery. arXiv.
35. Weighted-RXD and linear filter-based RXD: Improving background statistics estimation for anomaly detection in hyperspectral imagery10.1109/JSTARS.2014.2302446 ↗
36. Anomaly detection in hyperspectral images based on low-rank and sparse representation10.1109/TGRS.2015.2493201 ↗