PIDLF+: A Novel Adaptive PIDLF for Forestry Height Mapping Based on Single-Baseline PolInSAR Data
Forest height inversion based on polarimetric interferometric synthetic aperture radar data has demonstrated significant potential for producing large-scale high-resolution forest height maps and has been the subject of extensive research in recent decades. Machine learning and deep learning (DL) te...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11103723/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849737121347141632 |
|---|---|
| author | Dandan Li Hailiang Lu Mercedes E. Paoletti Juan M. Haut Juntao Gu Chao Li Weipeng Jing |
| author_facet | Dandan Li Hailiang Lu Mercedes E. Paoletti Juan M. Haut Juntao Gu Chao Li Weipeng Jing |
| author_sort | Dandan Li |
| collection | DOAJ |
| description | Forest height inversion based on polarimetric interferometric synthetic aperture radar data has demonstrated significant potential for producing large-scale high-resolution forest height maps and has been the subject of extensive research in recent decades. Machine learning and deep learning (DL) techniques frequently utilize the height of the forest derived from light detection and ranging (LiDAR) as labels, exhibiting superior inversion accuracy. Nevertheless, due to the complex terrain of forest areas, it is difficult to ensure the inversion accuracy when training in a specific forested scene and then applying it to other areas. Meanwhile, the lack of training samples imposes additional constraints on the efficacy of supervised DL-based methods. Given this context, an unsupervised framework for forest height inversion that can be used with several existing DL models and does not require previous knowledge from LiDAR data is very appealing. In this article, we delve into this framework by introducing a novel terrain compensation strategy, which allows the framework to be adapted to different forest terrain scenarios. Furthermore, a more efficient network architecture called ResUnet++ is used to extensively extract forest height information from pseudo-labels, resulting in the novel adaptive polarimetric interferometric deep-learning-based framework (<monospace>PIDLF+</monospace>). Three study sites with different terrain characteristics, i.e., Lopé, Pongara, and Rabi, were selected for validation. The experiments show that the proposed <monospace>PIDLF+</monospace> works well in different forest terrains and improves accuracy. It achieved a root-mean-square error (RMSE) of 8.69 m and a coefficient of determination (<inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula>) of 0.94 in the Lopé site, an RMSE of 11.31 m and an <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> of 0.90 in the Pongara site, and an RMSE of 8.86 m and an <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> of 0.92 in the Rabi site. |
| format | Article |
| id | doaj-art-c9f3d1edb33749f6b72d1db2c5795b6d |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-c9f3d1edb33749f6b72d1db2c5795b6d2025-08-20T03:07:01ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118200542007110.1109/JSTARS.2025.359420111103723PIDLF+: A Novel Adaptive PIDLF for Forestry Height Mapping Based on Single-Baseline PolInSAR DataDandan Li0https://orcid.org/0009-0004-7847-4449Hailiang Lu1https://orcid.org/0000-0003-0094-184XMercedes E. Paoletti2https://orcid.org/0000-0003-1030-3729Juan M. Haut3https://orcid.org/0000-0001-6701-961XJuntao Gu4Chao Li5https://orcid.org/0000-0003-1932-7698Weipeng Jing6https://orcid.org/0000-0001-7933-6946College of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaDepartment of Technology of Computers and Communications, University of Extremadura, Cáceres, SpainDepartment of Technology of Computers and Communications, University of Extremadura, Cáceres, SpainHeilongjiang Cyberspace Research Center, Harbin, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaForest height inversion based on polarimetric interferometric synthetic aperture radar data has demonstrated significant potential for producing large-scale high-resolution forest height maps and has been the subject of extensive research in recent decades. Machine learning and deep learning (DL) techniques frequently utilize the height of the forest derived from light detection and ranging (LiDAR) as labels, exhibiting superior inversion accuracy. Nevertheless, due to the complex terrain of forest areas, it is difficult to ensure the inversion accuracy when training in a specific forested scene and then applying it to other areas. Meanwhile, the lack of training samples imposes additional constraints on the efficacy of supervised DL-based methods. Given this context, an unsupervised framework for forest height inversion that can be used with several existing DL models and does not require previous knowledge from LiDAR data is very appealing. In this article, we delve into this framework by introducing a novel terrain compensation strategy, which allows the framework to be adapted to different forest terrain scenarios. Furthermore, a more efficient network architecture called ResUnet++ is used to extensively extract forest height information from pseudo-labels, resulting in the novel adaptive polarimetric interferometric deep-learning-based framework (<monospace>PIDLF+</monospace>). Three study sites with different terrain characteristics, i.e., Lopé, Pongara, and Rabi, were selected for validation. The experiments show that the proposed <monospace>PIDLF+</monospace> works well in different forest terrains and improves accuracy. It achieved a root-mean-square error (RMSE) of 8.69 m and a coefficient of determination (<inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula>) of 0.94 in the Lopé site, an RMSE of 11.31 m and an <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> of 0.90 in the Pongara site, and an RMSE of 8.86 m and an <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> of 0.92 in the Rabi site.https://ieeexplore.ieee.org/document/11103723/Deep learning (DL)forest height estimation<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$L$</tex-math> </inline-formula>-bandpolarimetric interferometric synthetic aperture radar (PolInSAR)terrain slope |
| spellingShingle | Dandan Li Hailiang Lu Mercedes E. Paoletti Juan M. Haut Juntao Gu Chao Li Weipeng Jing PIDLF+: A Novel Adaptive PIDLF for Forestry Height Mapping Based on Single-Baseline PolInSAR Data IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning (DL) forest height estimation <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$L$</tex-math> </inline-formula>-band polarimetric interferometric synthetic aperture radar (PolInSAR) terrain slope |
| title | PIDLF+: A Novel Adaptive PIDLF for Forestry Height Mapping Based on Single-Baseline PolInSAR Data |
| title_full | PIDLF+: A Novel Adaptive PIDLF for Forestry Height Mapping Based on Single-Baseline PolInSAR Data |
| title_fullStr | PIDLF+: A Novel Adaptive PIDLF for Forestry Height Mapping Based on Single-Baseline PolInSAR Data |
| title_full_unstemmed | PIDLF+: A Novel Adaptive PIDLF for Forestry Height Mapping Based on Single-Baseline PolInSAR Data |
| title_short | PIDLF+: A Novel Adaptive PIDLF for Forestry Height Mapping Based on Single-Baseline PolInSAR Data |
| title_sort | pidlf a novel adaptive pidlf for forestry height mapping based on single baseline polinsar data |
| topic | Deep learning (DL) forest height estimation <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$L$</tex-math> </inline-formula>-band polarimetric interferometric synthetic aperture radar (PolInSAR) terrain slope |
| url | https://ieeexplore.ieee.org/document/11103723/ |
| work_keys_str_mv | AT dandanli pidlfanoveladaptivepidlfforforestryheightmappingbasedonsinglebaselinepolinsardata AT hailianglu pidlfanoveladaptivepidlfforforestryheightmappingbasedonsinglebaselinepolinsardata AT mercedesepaoletti pidlfanoveladaptivepidlfforforestryheightmappingbasedonsinglebaselinepolinsardata AT juanmhaut pidlfanoveladaptivepidlfforforestryheightmappingbasedonsinglebaselinepolinsardata AT juntaogu pidlfanoveladaptivepidlfforforestryheightmappingbasedonsinglebaselinepolinsardata AT chaoli pidlfanoveladaptivepidlfforforestryheightmappingbasedonsinglebaselinepolinsardata AT weipengjing pidlfanoveladaptivepidlfforforestryheightmappingbasedonsinglebaselinepolinsardata |