Next Article in Journal
An Adaptive Surface Interpolation Filter Using Cloth Simulation and Relief Amplitude for Airborne Laser Scanning Data
Next Article in Special Issue
Retrieval of DTM under Complex Forest Stand Based on Spaceborne LiDAR Fusion Photon Correction
Previous Article in Journal
Optimization of Rocky Desertification Classification Model Based on Vegetation Type and Seasonal Characteristic
Previous Article in Special Issue
Sensitivity of C-Band SAR Polarimetric Variables to the Directionality of Surface Roughness Parameters
Article

Underlying Topography Inversion Using TomoSAR Based on Non-Local Means for an L-Band Airborne Dataset

1
School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
2
School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
3
School of Geoscience and Info-Physics, Central South University, Changsha 410083, China
4
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Academic Editors: Paavo Nevalainen, Fahimeh Farahnakian, Maarit Middleton and Jonne Pohjankukka
Remote Sens. 2021, 13(15), 2926; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152926
Received: 16 June 2021 / Revised: 15 July 2021 / Accepted: 23 July 2021 / Published: 26 July 2021
The underlying topography is an important part of the three-dimensional structure of forests, and is used for a variety of applications, such as hydrology and water resource management, civil engineering projects, and forest resource surveying. Due to the three-dimensional imaging ability and strong penetration, the tomographic synthetic aperture radar (TomoSAR) with a long wavelength has been shown to be a useful tool to estimate the underlying topography. At present, most of the current methods use the local means method to estimate the sample covariance matrix, in which the vertical backscattering power is estimated. However, these methods cannot easily obtain high-precision underlying topography, and often lose some detailed information. In this paper, to solve this problem, a non-local means method is introduced to estimate the optimal covariance matrix by combining weighted neighborhood pixels. To validate the feasibility and effectiveness of this proposed method, a BioSAR 2008 campaign L-band dataset acquired from the northern forests of Sweden was used to inverse the underlying topography. The results show that the accuracy of the underlying topography retrieved by the proposed method is improved by more than 30% when compared with the traditional method. View Full-Text
Keywords: underlying topography; tomographic synthetic aperture radar (TomoSAR); covariance matrix (CM); local means (LM); nonlocal means (NLM) underlying topography; tomographic synthetic aperture radar (TomoSAR); covariance matrix (CM); local means (LM); nonlocal means (NLM)
Show Figures

Graphical abstract

MDPI and ACS Style

Peng, X.; Wang, Y.; Long, S.; Pan, X.; Xie, Q.; Du, Y.; Fu, H.; Zhu, J.; Li, X. Underlying Topography Inversion Using TomoSAR Based on Non-Local Means for an L-Band Airborne Dataset. Remote Sens. 2021, 13, 2926. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152926

AMA Style

Peng X, Wang Y, Long S, Pan X, Xie Q, Du Y, Fu H, Zhu J, Li X. Underlying Topography Inversion Using TomoSAR Based on Non-Local Means for an L-Band Airborne Dataset. Remote Sensing. 2021; 13(15):2926. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152926

Chicago/Turabian Style

Peng, Xing, Youjun Wang, Shilin Long, Xiong Pan, Qinghua Xie, Yanan Du, Haiqiang Fu, Jianjun Zhu, and Xinwu Li. 2021. "Underlying Topography Inversion Using TomoSAR Based on Non-Local Means for an L-Band Airborne Dataset" Remote Sensing 13, no. 15: 2926. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152926

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop