A novel spaceborne photon-counting laser altimeter denoising method based on parameter-adaptive density clustering
To tackle the challenge of denoising spaceborne photon-counting laser altimeter point clouds with uneven noise density, this study proposes a denoising method based on adaptive parameter density clustering, which utilizes numerical simulations to achieve self-adaptation of key parameters (neighborho...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2024.2326702 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850135431260143616 |
|---|---|
| author | Ren Liu Xinming Tang Junfeng Xie Rujia Ma Fan Mo Xiaomeng Yang |
| author_facet | Ren Liu Xinming Tang Junfeng Xie Rujia Ma Fan Mo Xiaomeng Yang |
| author_sort | Ren Liu |
| collection | DOAJ |
| description | To tackle the challenge of denoising spaceborne photon-counting laser altimeter point clouds with uneven noise density, this study proposes a denoising method based on adaptive parameter density clustering, which utilizes numerical simulations to achieve self-adaptation of key parameters (neighborhood radius [Formula: see text] and minimum number of points [Formula: see text]). First, taking the directional adaptive ellipse DBSCAN (DAE-DBSCAN) as an example, photons with different background photon count rates ([Formula: see text]) are used to traverse [Formula: see text] and [Formula: see text] to calculate their optimal values ([Formula: see text] and [Formula: see text] with the highest denoising accuracy). Then, a mathematical prediction model of [Formula: see text], [Formula: see text] and [Formula: see text] was established. The actual background photon count rates were introduced into the key parameter prediction model to obtain the optimal [Formula: see text] and [Formula: see text]. Finally, a denoising experiment was conducted using the simulated photons and the ATLAS data. The results show that the proposed method had higher accuracy than the constant parameter denoising method, with an [Formula: see text] >0.95. Even for photons of complex mountainous terrain with a high background photon count rate, the denoising accuracy was still higher than 0.9. The proposed method improves the denoising accuracy of photons with different noise densities by adapting density clustering parameters. |
| format | Article |
| id | doaj-art-0b2e853ffb894e9f908f015e66e821aa |
| institution | OA Journals |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj-art-0b2e853ffb894e9f908f015e66e821aa2025-08-20T02:31:26ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262024-12-0161110.1080/15481603.2024.2326702A novel spaceborne photon-counting laser altimeter denoising method based on parameter-adaptive density clusteringRen Liu0Xinming Tang1Junfeng Xie2Rujia Ma3Fan Mo4Xiaomeng Yang5Faculty of Geography, Yunnan Normal University, Kunming, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing, ChinaHangzhou Institute for Advanced Study, the Chinese Academy of Sciences, Hangzhou, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing, ChinaTo tackle the challenge of denoising spaceborne photon-counting laser altimeter point clouds with uneven noise density, this study proposes a denoising method based on adaptive parameter density clustering, which utilizes numerical simulations to achieve self-adaptation of key parameters (neighborhood radius [Formula: see text] and minimum number of points [Formula: see text]). First, taking the directional adaptive ellipse DBSCAN (DAE-DBSCAN) as an example, photons with different background photon count rates ([Formula: see text]) are used to traverse [Formula: see text] and [Formula: see text] to calculate their optimal values ([Formula: see text] and [Formula: see text] with the highest denoising accuracy). Then, a mathematical prediction model of [Formula: see text], [Formula: see text] and [Formula: see text] was established. The actual background photon count rates were introduced into the key parameter prediction model to obtain the optimal [Formula: see text] and [Formula: see text]. Finally, a denoising experiment was conducted using the simulated photons and the ATLAS data. The results show that the proposed method had higher accuracy than the constant parameter denoising method, with an [Formula: see text] >0.95. Even for photons of complex mountainous terrain with a high background photon count rate, the denoising accuracy was still higher than 0.9. The proposed method improves the denoising accuracy of photons with different noise densities by adapting density clustering parameters.https://www.tandfonline.com/doi/10.1080/15481603.2024.2326702ICESat-2photon denoisingDBSCANphoton data simulationparameter adaptive |
| spellingShingle | Ren Liu Xinming Tang Junfeng Xie Rujia Ma Fan Mo Xiaomeng Yang A novel spaceborne photon-counting laser altimeter denoising method based on parameter-adaptive density clustering GIScience & Remote Sensing ICESat-2 photon denoising DBSCAN photon data simulation parameter adaptive |
| title | A novel spaceborne photon-counting laser altimeter denoising method based on parameter-adaptive density clustering |
| title_full | A novel spaceborne photon-counting laser altimeter denoising method based on parameter-adaptive density clustering |
| title_fullStr | A novel spaceborne photon-counting laser altimeter denoising method based on parameter-adaptive density clustering |
| title_full_unstemmed | A novel spaceborne photon-counting laser altimeter denoising method based on parameter-adaptive density clustering |
| title_short | A novel spaceborne photon-counting laser altimeter denoising method based on parameter-adaptive density clustering |
| title_sort | novel spaceborne photon counting laser altimeter denoising method based on parameter adaptive density clustering |
| topic | ICESat-2 photon denoising DBSCAN photon data simulation parameter adaptive |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2024.2326702 |
| work_keys_str_mv | AT renliu anovelspacebornephotoncountinglaseraltimeterdenoisingmethodbasedonparameteradaptivedensityclustering AT xinmingtang anovelspacebornephotoncountinglaseraltimeterdenoisingmethodbasedonparameteradaptivedensityclustering AT junfengxie anovelspacebornephotoncountinglaseraltimeterdenoisingmethodbasedonparameteradaptivedensityclustering AT rujiama anovelspacebornephotoncountinglaseraltimeterdenoisingmethodbasedonparameteradaptivedensityclustering AT fanmo anovelspacebornephotoncountinglaseraltimeterdenoisingmethodbasedonparameteradaptivedensityclustering AT xiaomengyang anovelspacebornephotoncountinglaseraltimeterdenoisingmethodbasedonparameteradaptivedensityclustering AT renliu novelspacebornephotoncountinglaseraltimeterdenoisingmethodbasedonparameteradaptivedensityclustering AT xinmingtang novelspacebornephotoncountinglaseraltimeterdenoisingmethodbasedonparameteradaptivedensityclustering AT junfengxie novelspacebornephotoncountinglaseraltimeterdenoisingmethodbasedonparameteradaptivedensityclustering AT rujiama novelspacebornephotoncountinglaseraltimeterdenoisingmethodbasedonparameteradaptivedensityclustering AT fanmo novelspacebornephotoncountinglaseraltimeterdenoisingmethodbasedonparameteradaptivedensityclustering AT xiaomengyang novelspacebornephotoncountinglaseraltimeterdenoisingmethodbasedonparameteradaptivedensityclustering |