Preliminary verification of hyperspectral LiDAR covering VIS-NIR-SWIR used for objects classification
Hyperspectral LiDAR (HSL) has been utilised as an efficacious technique in objects classification and recognition based on its synchronously obtaining spectral and spatial information. However, the spectral information obtained by most of the developed HSL was in the visible and near-infrared range...
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
|
| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2022.2056519 |
| _version_ | 1811329869540229120 |
|---|---|
| author | Haibin Sun Zhen Wang Yuwei Chen Wenxin Tian Wenjing He Haohao Wu Huijing Zhang Lingli Tang Changhui Jiang Jianxin Jia Zhiyong Duan Hui Zhou Eetu Puttonen Juha Hyyppä |
| author_facet | Haibin Sun Zhen Wang Yuwei Chen Wenxin Tian Wenjing He Haohao Wu Huijing Zhang Lingli Tang Changhui Jiang Jianxin Jia Zhiyong Duan Hui Zhou Eetu Puttonen Juha Hyyppä |
| author_sort | Haibin Sun |
| collection | DOAJ |
| description | Hyperspectral LiDAR (HSL) has been utilised as an efficacious technique in objects classification and recognition based on its synchronously obtaining spectral and spatial information. However, the spectral information obtained by most of the developed HSL was in the visible and near-infrared range (VNIR, 400–1000 nm). Whereas spectral information in a longer wavelength range showed more useful for classification and detection, such as detecting vegetation water content. This paper proposed and tested an eight-channel HSL prototype covering visible to near-infrared and even short-wavelength infrared (VIS-NIR-SWIR, 450–1460 nm) based on a Super-continuum (SC) laser. System calibration, range precision and spectral profiles experiments were carried out to test the HSL prototype. The spectral profiles collected by the HSL are consistent with those acquired by the commercial spectrometer (SVC© HR-1024). And these spectral profiles of plants, textiles, camouflage objects, and ore samples collected by the HSL, especially those in the SWIR range, can effectively reveal the health status of the plants, and classify the manufacturing materials and ore species. The unique characteristics of spectral profiles covering VIS-NIR-SWIR promote the HSL shows the potential applications on objects classification related to vegetation, mining and surveillance. |
| first_indexed | 2024-04-13T15:51:32Z |
| format | Article |
| id | doaj.art-fed04426c13e4572995dcc19137710cc |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-04-13T15:51:32Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-fed04426c13e4572995dcc19137710cc2022-12-22T02:40:49ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542022-12-0155129130310.1080/22797254.2022.2056519Preliminary verification of hyperspectral LiDAR covering VIS-NIR-SWIR used for objects classificationHaibin Sun0Zhen Wang1Yuwei Chen2Wenxin Tian3Wenjing He4Haohao Wu5Huijing Zhang6Lingli Tang7Changhui Jiang8Jianxin Jia9Zhiyong Duan10Hui Zhou11Eetu Puttonen12Juha Hyyppä13Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Kirkkonummi, FinlandKey Laboratory of Quantitative Remote Sensing Information Technology, Chinese Academy Science (CAS), and Earth Observation Technology Application Department (ETA), Academy of Opto-Electronics (AOE), Chinese Academy Science (CAS), Beijing, ChinaDepartment of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Kirkkonummi, FinlandKey Laboratory of Quantitative Remote Sensing Information Technology, Chinese Academy Science (CAS), and Earth Observation Technology Application Department (ETA), Academy of Opto-Electronics (AOE), Chinese Academy Science (CAS), Beijing, ChinaKey Laboratory of Quantitative Remote Sensing Information Technology, Chinese Academy Science (CAS), and Earth Observation Technology Application Department (ETA), Academy of Opto-Electronics (AOE), Chinese Academy Science (CAS), Beijing, ChinaKey Laboratory of Quantitative Remote Sensing Information Technology, Chinese Academy Science (CAS), and Earth Observation Technology Application Department (ETA), Academy of Opto-Electronics (AOE), Chinese Academy Science (CAS), Beijing, ChinaKey Laboratory of Quantitative Remote Sensing Information Technology, Chinese Academy Science (CAS), and Earth Observation Technology Application Department (ETA), Academy of Opto-Electronics (AOE), Chinese Academy Science (CAS), Beijing, ChinaKey Laboratory of Quantitative Remote Sensing Information Technology, Chinese Academy Science (CAS), and Earth Observation Technology Application Department (ETA), Academy of Opto-Electronics (AOE), Chinese Academy Science (CAS), Beijing, ChinaDepartment of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Kirkkonummi, FinlandDepartment of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Kirkkonummi, FinlandDepartment of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Kirkkonummi, FinlandElectronic Information School, Wuhan University, Wuhan, ChinaDepartment of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Kirkkonummi, FinlandDepartment of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Kirkkonummi, FinlandHyperspectral LiDAR (HSL) has been utilised as an efficacious technique in objects classification and recognition based on its synchronously obtaining spectral and spatial information. However, the spectral information obtained by most of the developed HSL was in the visible and near-infrared range (VNIR, 400–1000 nm). Whereas spectral information in a longer wavelength range showed more useful for classification and detection, such as detecting vegetation water content. This paper proposed and tested an eight-channel HSL prototype covering visible to near-infrared and even short-wavelength infrared (VIS-NIR-SWIR, 450–1460 nm) based on a Super-continuum (SC) laser. System calibration, range precision and spectral profiles experiments were carried out to test the HSL prototype. The spectral profiles collected by the HSL are consistent with those acquired by the commercial spectrometer (SVC© HR-1024). And these spectral profiles of plants, textiles, camouflage objects, and ore samples collected by the HSL, especially those in the SWIR range, can effectively reveal the health status of the plants, and classify the manufacturing materials and ore species. The unique characteristics of spectral profiles covering VIS-NIR-SWIR promote the HSL shows the potential applications on objects classification related to vegetation, mining and surveillance.https://www.tandfonline.com/doi/10.1080/22797254.2022.2056519Remote sensingclassificationhyperspectral LiDARSWIR spectrumsuper-continuum lasergaussian fitting |
| spellingShingle | Haibin Sun Zhen Wang Yuwei Chen Wenxin Tian Wenjing He Haohao Wu Huijing Zhang Lingli Tang Changhui Jiang Jianxin Jia Zhiyong Duan Hui Zhou Eetu Puttonen Juha Hyyppä Preliminary verification of hyperspectral LiDAR covering VIS-NIR-SWIR used for objects classification European Journal of Remote Sensing Remote sensing classification hyperspectral LiDAR SWIR spectrum super-continuum laser gaussian fitting |
| title | Preliminary verification of hyperspectral LiDAR covering VIS-NIR-SWIR used for objects classification |
| title_full | Preliminary verification of hyperspectral LiDAR covering VIS-NIR-SWIR used for objects classification |
| title_fullStr | Preliminary verification of hyperspectral LiDAR covering VIS-NIR-SWIR used for objects classification |
| title_full_unstemmed | Preliminary verification of hyperspectral LiDAR covering VIS-NIR-SWIR used for objects classification |
| title_short | Preliminary verification of hyperspectral LiDAR covering VIS-NIR-SWIR used for objects classification |
| title_sort | preliminary verification of hyperspectral lidar covering vis nir swir used for objects classification |
| topic | Remote sensing classification hyperspectral LiDAR SWIR spectrum super-continuum laser gaussian fitting |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2022.2056519 |
| work_keys_str_mv | AT haibinsun preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification AT zhenwang preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification AT yuweichen preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification AT wenxintian preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification AT wenjinghe preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification AT haohaowu preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification AT huijingzhang preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification AT linglitang preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification AT changhuijiang preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification AT jianxinjia preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification AT zhiyongduan preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification AT huizhou preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification AT eetuputtonen preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification AT juhahyyppa preliminaryverificationofhyperspectrallidarcoveringvisnirswirusedforobjectsclassification |