Estimation of subcanopy topography based on single-baseline TanDEM-X InSAR data

Wang, Huiqiang; Fu, Haiqiang; Zhu, Jianjun; Liu, Zhiwei; Zhang, Bing; Wang, Changcheng; Li, Zhiwei; Hu, Jun; Yu, Yanan

doi:10.1007/s00190-021-01519-3

Cited by 11 publications

(12 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the powerful feature extraction of the encoderdecoder architecture and the use of nonlocal phase information, NL-PFNet can predict the accurate real and imaginary parts of the interferometric phase after training using a large number of interferometric phase images with different noise levels. Finally, the filtered interferometric phase can be obtained by (5). Then, NL-PFNet will be introduced in detail.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…According to (5), we can use a neural network to predict the filtered real and imaginary parts of the interferometric phase and then calculate the filtered interferometric phase. Following this processing idea, some methods [16,17] have successfully used DCNNs to achieve phase filtering and rely on the powerful feature extraction capabilities of DCNNs to obtain a filtering performance beyond traditional phase filtering methods to a certain extent, but these methods are achieved based on local neighboring pixels and only use local phase information.…”

Section: Problem Analysismentioning

confidence: 99%

“…Interferometric synthetic aperture radar (InSAR) is becoming increasingly important in the field of remote sensing and has been successfully applied in topography mapping and deformation monitoring [1][2][3][4][5]. In the InSAR data processing pipeline, the interferometric phase is formed by two or more SAR complex images acquired at different viewing angles or at different times.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nonlocal Feature Selection Encoder–Decoder Network for Accurate InSAR Phase Filtering

Zhou

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Accurate interferometric phase filtering is an essential step in InSAR data processing. The existing deep learning-based phase-filtering methods were developed based on local neighboring pixels and only use local phase information. The idea of nonlocal processing has been proven to be very effective for improving the accuracy of interferometric phase filtering. In this paper, we propose a deep convolutional neural network-based nonlocal InSAR filtering method via a nonlocal phase filtering network (NL-PFNet) based on the encoder–decoder structure and nonlocal feature selection strategy. Thanks to the powerful phase feature extraction ability of the encoder–decoder structure and the utilization of nonlocal phase information, NL-PFNet can predict an accurately filtered interferometric phase after training using a large number of interferometric phase images with different noise levels. Experiments on both simulated and real InSAR data show that the proposed method significantly outperforms three traditional well-established methods and another deep learning-based method. Compared with the InSAR-BM3D filter and another deep learning-based method, the mean square error of the proposed method is 25% and 11% lower when processing simulated data, respectively, and when processing the real Sentinel-1 interferometric phase, the no-reference evaluation metric Q of the proposed method is 25% and 9% higher, respectively. In addition, the running time of the proposed method is tens of times less than that of the traditional filtering methods.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Section: Problem Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonlocal Feature Selection Encoder–Decoder Network for Accurate InSAR Phase Filtering

Zhou

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…One of the effective schemes to solve the issue of insufficient observations is using external data, such as a precise DTM for the ground topography [21]. However, accurate DTM covering wide areas are usually unavailable for forested regions [21], [31], [32]. Another strategy consists in simplifying the RVoG model by applying diverse assumptions.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Canopy Height From a Multi-SINC Model in Mediterranean Forest With Single-Baseline TanDEM-X InSAR Data

Zhang,

Fu,

Zhu

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

TanDEM-X interferometric synthetic aperture radar (InSAR) data have demonstrated promising advantages and potential in recent years for the inversion of forest height. InSAR coherence becomes the primary input feature when a precise digital terrain model (DTM) is unavailable, but the relationship between InSAR coherence and forest height remains uncertain because of the complexity of forest scenes. In this paper, a method for retrieving canopy height in Mediterranean forests, characterised by short and sparse trees, using a single-pass bistatic TanDEM-X InSAR dataset is proposed. To improve the accuracy of forest height inversion from the uncertain correlation between InSAR coherence and canopy height, we begin by using the established SINC model with two semi-empirical parameters and then expand the single curve into a collection of three curves, forming the Multi-SINC model. To determine the optimal relationship (curve) between TanDEM-X InSAR coherence and canopy height, the problem is shifted from parameter inversion to classification. To solve the problem, we used optical remote sensing data, a small amount of LiDAR data, and TanDEM-X InSAR data in combination with machine learning for classification. As a proof-of-concept, we conducted forest height retrieval at two study sites in Spain with complex terrain and diverse forest types. The results were verified by comparing them with LiDAR product forest height, which demonstrated improved performance (RMSE = 2.49 m and 1.7 m) compared to the SeEm-SINC model (RMSE = 3.28 m and 2.36 m).

show abstract

“…However, owing to its limited penetrability, the DEM obtained from TanDEM-X InSAR data contains significant Remote Sens. 2024, 16, 1155 2 of 19 forest height signals [9]. Consequently, the obtained DEM does not reflect sub-canopy topography, which is important for many applications.…”

Section: Introductionmentioning

confidence: 99%

Automated Estimation of Sub-Canopy Topography Combined with Single-Baseline Single-Polarization TanDEM-X InSAR and ICESat-2 Data

Hu,

Zhu,

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

TanDEM-X bistatic interferometric system successfully generated a high-precision, high-resolution global digital elevation model (DEM). However, in forested areas, two core problems make it difficult to obtain sub-canopy topography: (1) the penetrability of short-wave signals is limited, and the DEM obtained in dense forest areas contains a significant forest signal, that is, the scattering phase center (SPC) height; and (2) the single-baseline and single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data cannot provide sufficient observations to make the existing physical model reversible for estimating the real surface phase, whereas the introduction of optical data makes it difficult to ensure data synchronization and availability of cloud-free data. To overcome these problems in accurately estimating sub-canopy topography from TanDEM-X InSAR data, this study proposes a practical method of sub-canopy topography estimation based on the following innovations: (1) An orthogonal polynomial model was established using TanDEM-X interferometric coherence and slope to estimate the SPC height. Interferometric coherence records forest height and dielectric property information from an InSAR perspective and has spatiotemporal consistency with the InSAR-derived DEM. (2) Introduce Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data to provide more observational information and automatically screen ICESat-2 control points with similar forest and slope conditions in the local area to suppress forest spatial heterogeneity. (3) A weighted least squares criterion was used to solve this model to estimate the SPC height. The results were validated at four test sites using high-precision airborne light detection and ranging (LiDAR) data as a reference. Compared to the InSAR-derived DEM, the accuracy of the sub-canopy topography was improved by nearly 60%, on average. Furthermore, we investigated the necessity of local modeling, confirming the potential of the proposed method for estimating sub-canopy topography by relying only on TanDEM-X and ICESat-2 data.

show abstract

Estimation of subcanopy topography based on single-baseline TanDEM-X InSAR data

Cited by 11 publications

References 65 publications

Nonlocal Feature Selection Encoder–Decoder Network for Accurate InSAR Phase Filtering

Nonlocal Feature Selection Encoder–Decoder Network for Accurate InSAR Phase Filtering

Estimation of Canopy Height From a Multi-SINC Model in Mediterranean Forest With Single-Baseline TanDEM-X InSAR Data

Automated Estimation of Sub-Canopy Topography Combined with Single-Baseline Single-Polarization TanDEM-X InSAR and ICESat-2 Data

Contact Info

Product

Resources

About