Hyperspectral Target Detection Based on Prior Spectral Perception and Local Graph Fusion
With the development of hyperspectral sensing technology, hyperspectral target detection technology plays an important role in remote target detection. However, existing hyperspectral target detection models are poorly adapted to complex backgrounds and mainly focus on the spectral domain, making le...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10623901/ |
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| _version_ | 1850051867991605248 |
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| author | Xiaobin Zhao Jun Huang Yunquan Gao Qingwang Wang |
| author_facet | Xiaobin Zhao Jun Huang Yunquan Gao Qingwang Wang |
| author_sort | Xiaobin Zhao |
| collection | DOAJ |
| description | With the development of hyperspectral sensing technology, hyperspectral target detection technology plays an important role in remote target detection. However, existing hyperspectral target detection models are poorly adapted to complex backgrounds and mainly focus on the spectral domain, making less use of spatial structure information leading to low target detection rates. Therefore, a new target detection algorithm based on the prior spectral perception and local graph fusion is proposed. First, the prior spectrum-guided target extraction method is established. This method can take full advantage of the background and target spectral information by local inner and outer window linkage, reduce the impact of spectral variability on target acquisition performance, and improve detection stability. Second, the target enhancement strategy based on the Gabor multifeature graph is proposed. This technique makes full use of multidirectional and multiscale spatial information, which can reduce the influence of brightness, contrast and amplitude variation on detection performance due to light and angle. Finally, spatial–spectral fusion is executed to achieve target detection. It can make full use of spectral and spatial structure information to improve the target detection effect. Publicly available datasets and real collected datasets are adopted to check the validity of the proposed method. After comparison, it is found that the proposed algorithm has better detection effect than existing baseline methods. The maximum improvement in AUC values are 16.56%–88.16% across the eight datasets. |
| format | Article |
| id | doaj-art-d349c585ee3a4cbea89a622a91e37c9a |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-d349c585ee3a4cbea89a622a91e37c9a2025-08-20T02:52:59ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117139361394810.1109/JSTARS.2024.343956010623901Hyperspectral Target Detection Based on Prior Spectral Perception and Local Graph FusionXiaobin Zhao0https://orcid.org/0000-0002-9828-1976Jun Huang1https://orcid.org/0000-0002-2022-5747Yunquan Gao2Qingwang Wang3https://orcid.org/0000-0001-5820-5357Beijing Key Laboratory of Fractional Signals and Systems, School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaAnhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet, Anhui University of Technology, Ma'anshan, ChinaAnhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet, Anhui University of Technology, Ma'anshan, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, ChinaWith the development of hyperspectral sensing technology, hyperspectral target detection technology plays an important role in remote target detection. However, existing hyperspectral target detection models are poorly adapted to complex backgrounds and mainly focus on the spectral domain, making less use of spatial structure information leading to low target detection rates. Therefore, a new target detection algorithm based on the prior spectral perception and local graph fusion is proposed. First, the prior spectrum-guided target extraction method is established. This method can take full advantage of the background and target spectral information by local inner and outer window linkage, reduce the impact of spectral variability on target acquisition performance, and improve detection stability. Second, the target enhancement strategy based on the Gabor multifeature graph is proposed. This technique makes full use of multidirectional and multiscale spatial information, which can reduce the influence of brightness, contrast and amplitude variation on detection performance due to light and angle. Finally, spatial–spectral fusion is executed to achieve target detection. It can make full use of spectral and spatial structure information to improve the target detection effect. Publicly available datasets and real collected datasets are adopted to check the validity of the proposed method. After comparison, it is found that the proposed algorithm has better detection effect than existing baseline methods. The maximum improvement in AUC values are 16.56%–88.16% across the eight datasets.https://ieeexplore.ieee.org/document/10623901/Hyperspectral target detectionlocal graph (LG)remote sensingspatial–spectral fusionspectral perception (SP) |
| spellingShingle | Xiaobin Zhao Jun Huang Yunquan Gao Qingwang Wang Hyperspectral Target Detection Based on Prior Spectral Perception and Local Graph Fusion IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Hyperspectral target detection local graph (LG) remote sensing spatial–spectral fusion spectral perception (SP) |
| title | Hyperspectral Target Detection Based on Prior Spectral Perception and Local Graph Fusion |
| title_full | Hyperspectral Target Detection Based on Prior Spectral Perception and Local Graph Fusion |
| title_fullStr | Hyperspectral Target Detection Based on Prior Spectral Perception and Local Graph Fusion |
| title_full_unstemmed | Hyperspectral Target Detection Based on Prior Spectral Perception and Local Graph Fusion |
| title_short | Hyperspectral Target Detection Based on Prior Spectral Perception and Local Graph Fusion |
| title_sort | hyperspectral target detection based on prior spectral perception and local graph fusion |
| topic | Hyperspectral target detection local graph (LG) remote sensing spatial–spectral fusion spectral perception (SP) |
| url | https://ieeexplore.ieee.org/document/10623901/ |
| work_keys_str_mv | AT xiaobinzhao hyperspectraltargetdetectionbasedonpriorspectralperceptionandlocalgraphfusion AT junhuang hyperspectraltargetdetectionbasedonpriorspectralperceptionandlocalgraphfusion AT yunquangao hyperspectraltargetdetectionbasedonpriorspectralperceptionandlocalgraphfusion AT qingwangwang hyperspectraltargetdetectionbasedonpriorspectralperceptionandlocalgraphfusion |