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Synthetic aperture radar tomography (TomoSAR) has been proven to be a useful way to reconstruct vertical structure over forest areas with P-band images, on account of its three-dimensional imaging ability. In the case of a small number of non-uniformly distributed acquisitions, compressive sensing (CS) is generally adopted in TomoSAR. However, the performance of CS depends on the selected hyperparameter, which is closely related to the noise of a pixel. In this paper, to overcome this limitation, we propose a sparse iterative covariance-based estimation (SPICE) approach based on the wavelet and orthogonal sparse basis (W&O-SPICE) for application over forest areas. SPICE is a sparse spectral estimation method that achieves a high vertical resolution, and takes account of the noise adaptively for each resolution cell. Thus, it does not require the user to select a hyperparameter. Furthermore, the used sparse basis not only ensures the sparsity of the forest canopy scattering contribution, but it can also keep the original sparse information of the ground contribution. The proposed method was tested in simulated experiments and the results demonstrated that W&O-SPICE can successfully reconstruct the vertical structure of a forest. Moreover, three P-band fully polarimetric airborne SAR images with non-uniformly distributed baselines were applied to reconstruct the vertical structure of a tropical forest in Mabounie, Gabon. The underlying topography and forest height were estimated, and the root-mean-square errors (RMSEs) were 6.40 m and 4.50 m with respect to the LiDAR digital terrain model (DTM) and canopy height model (CHM), respectively. In addition, W&O-SPICE showed a better performance than W&O-CS, beamforming, Capon, and the iterative adaptive approach (IAA). [ABSTRACT FROM AUTHOR]

Underground extraction tends to cause surface deformation, imposing progressive damage onbuildings located in deformation regions during the whole period of mining. This paperpresents a method for assessing mining-induced progressive damage on buildings based onInSAR and a mining-related deformation model named temporal probability integralmethod (TPIM). This method firstly inverses the model parameters of TPIM withInSAR-derived deformation measurements along the radar line-of-sight direction. Thenthree-dimensional dynamic displacements caused by future underground mining arepredicted with the TPIM and its inverted model parameters. Finally, progressivedamage on buildings associated with future underground extraction are assessed basedon the predicted three-dimensional dynamic displacements. The feasibility andreliability of the proposed method were tested in Huaibei coal mining area, China. [ABSTRACT FROM AUTHOR]

Mining-related deformations are the direct cause for most of geohazards (e.g., landsides, building damage, and aquifer broken). Limited by available number of current space-borne SAR sensors and their near-polar flighting configuration, previous InSAR methods based on multi-track SAR datasets are hardly to performed to accurately retrieve mining-induced 3-D time-series displacements. This paper presents a method for retrieving mining-induced 3-D time-series displacements from single-track SAR datasets by fusing a mining-related prior deformation model. Compared with the previous multi-track SAR-based methods, the presented method has much less requirement on SAR data and can significantly improves the accuracy of estimated time-series deformations in the north direction. Finally, 13 ascending TerraSAR-X images over Yulin coal mining area, China, were selected to test the proposed method. [ABSTRACT FROM AUTHOR]