Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN method
The Huanglong Scenic and Historic Interest Area in China, a UNESCO World Heritage Site, is famous for its large-scale, diverse, intricately structured, and brightly colored surface travertine landscapes. However, severe degradation of the Huanglong travertine formations, such as blackening and algae...
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Taylor & Francis Group
2024-01-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2024.2365886 |
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| author | Menghui Xu Weihong Wang Jialun Cai Qunwei Dai Jing Fan Sicheng Li |
| author_facet | Menghui Xu Weihong Wang Jialun Cai Qunwei Dai Jing Fan Sicheng Li |
| author_sort | Menghui Xu |
| collection | DOAJ |
| description | The Huanglong Scenic and Historic Interest Area in China, a UNESCO World Heritage Site, is famous for its large-scale, diverse, intricately structured, and brightly colored surface travertine landscapes. However, severe degradation of the Huanglong travertine formations, such as blackening and algae erosion, has occurred in recent years, necessitating monitoring and identification. We collected hyperspectral reflectance data of the travertine formations in different states and bare ground using a ground-based hyperspectral radiometer (PSR-2500) from ASD company. After conducting a correlation analysis between the hyperspectral reflectance data and the travertine formations, we identified healthy travertine formations, blackened travertine formations, travertine formations affected by algae erosion, and bare ground. The Siamese network method was employed to generate data labels, and the spectral features of the travertine formations were extracted by combining the sensitive bands with pre-processed and reduced data. The PSO-BPNN classifier was developed by optimizing the back propagation neural network (BPNN) using the particle swarm optimization algorithm (PSO). To verify the effectiveness of PSO-BPNN in accurately distinguishing different states of travertine formations, we compared its performance with that of BPNN using three performance indices. Finally, the proposed method was applied to the real-world hyperspectral image data collected by the Micro-Hyperspectral imaging instrument to classify the travertine formations in different states and bare ground. The test set demonstrated good overall performance, with an average overall accuracy (OA) of 0.93, F1-score of 0.92, and Kappa coefficient of 0.97. |
| format | Article |
| id | doaj-art-89ffdfa167894dc58776a4266bc1ad78 |
| institution | OA Journals |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-89ffdfa167894dc58776a4266bc1ad782025-08-20T02:38:26ZengTaylor & Francis GroupGeocarto International1010-60491752-07622024-01-0139110.1080/10106049.2024.2365886Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN methodMenghui Xu0Weihong Wang1Jialun Cai2Qunwei Dai3Jing Fan4Sicheng Li5School of Environment and Resources, Southwest University of Science and Technology, Mianyang, ChinaSchool of Environment and Resources, Southwest University of Science and Technology, Mianyang, ChinaSchool of Environment and Resources, Southwest University of Science and Technology, Mianyang, ChinaSchool of Environment and Resources, Southwest University of Science and Technology, Mianyang, ChinaSchool of Environment and Resources, Southwest University of Science and Technology, Mianyang, ChinaSchool of Environment and Resources, Southwest University of Science and Technology, Mianyang, ChinaThe Huanglong Scenic and Historic Interest Area in China, a UNESCO World Heritage Site, is famous for its large-scale, diverse, intricately structured, and brightly colored surface travertine landscapes. However, severe degradation of the Huanglong travertine formations, such as blackening and algae erosion, has occurred in recent years, necessitating monitoring and identification. We collected hyperspectral reflectance data of the travertine formations in different states and bare ground using a ground-based hyperspectral radiometer (PSR-2500) from ASD company. After conducting a correlation analysis between the hyperspectral reflectance data and the travertine formations, we identified healthy travertine formations, blackened travertine formations, travertine formations affected by algae erosion, and bare ground. The Siamese network method was employed to generate data labels, and the spectral features of the travertine formations were extracted by combining the sensitive bands with pre-processed and reduced data. The PSO-BPNN classifier was developed by optimizing the back propagation neural network (BPNN) using the particle swarm optimization algorithm (PSO). To verify the effectiveness of PSO-BPNN in accurately distinguishing different states of travertine formations, we compared its performance with that of BPNN using three performance indices. Finally, the proposed method was applied to the real-world hyperspectral image data collected by the Micro-Hyperspectral imaging instrument to classify the travertine formations in different states and bare ground. The test set demonstrated good overall performance, with an average overall accuracy (OA) of 0.93, F1-score of 0.92, and Kappa coefficient of 0.97.https://www.tandfonline.com/doi/10.1080/10106049.2024.2365886Travertinehyperspectralspectral feature extractionparticle swarm algorithmback propagation neural network |
| spellingShingle | Menghui Xu Weihong Wang Jialun Cai Qunwei Dai Jing Fan Sicheng Li Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN method Geocarto International Travertine hyperspectral spectral feature extraction particle swarm algorithm back propagation neural network |
| title | Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN method |
| title_full | Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN method |
| title_fullStr | Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN method |
| title_full_unstemmed | Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN method |
| title_short | Hyperspectral identification of travertine state in Huanglong by the PSO-BPNN method |
| title_sort | hyperspectral identification of travertine state in huanglong by the pso bpnn method |
| topic | Travertine hyperspectral spectral feature extraction particle swarm algorithm back propagation neural network |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2024.2365886 |
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