Aerial and terrestrial laser scanning (TLS) technologies offer complementary, high-precision 3-D data on forest structure. Registering point cloud data from multiplatform is crucial for achieving a more comprehensive understanding of forest structure. Currently, multisource forest point cloud registration remains challenging due to factors such as unstable point- and object-level features, varying observation perspectives, and nonstandardized processing pipelines. To address these challenges, this study introduces a unified and automated method for registering aerial and ground-based point clouds in forest areas. First, keypoints are extracted from multisource point clouds using fuzzy c-means (FCM) clustering. Local transformation is then derived from the keypoint sets using the gradient-based local convergence (GLC) algorithm, which is integrated with a nested branch and bound (BnB) structure for further optimization. We also developed a novel distribution similarity index (DSI) to evaluate the alignment of the keypoint sets and determine the initial transformation. Finally, this initial transformation is applied to the ground-based point cloud and refined using the GLC algorithm. Compared with existing methods that rely on point and object features, the proposed method does not depend on geometric descriptions or tree attributes (e.g., tree position and canopy structure). It also demonstrates robustness to variations in the initial position of the point clouds. Testing on 15 datasets with varying plot sizes and tree characteristics shows that the proposed method achieves comparable or superior performance to existing methods, with an average distance residual of 6.59 cm and an average runtime of 90.04 s.

Accurately identifying rock mass discontinuities and understanding distribution, characteristics, and properties are crucial for assessing slope stability and mitigating the risk of collapse or sliding. The automated identification of discontinuities efficiently provides valuable information such as spacing and volumetric joint count. However, determining the optimal number of clusters automatically for complex rock mass discontinuities remains a technical bottleneck. To address this difficulty, this study introduces an approach based on the fast search and find density peaks (3D-SFDP) algorithm, which autonomously determines the number of clusters by analysing spatial density distributions. Furthermore, traditional clustering algorithms struggle with nonspherical clusters when identifying rock discontinuities using 3D point clouds. In this study, a 3D Gaussian mixture model (3D-GMM) based on 3D pole projection density mapping is presented. This model is designed for handling non-spherical clustering scenarios. Combined with the DBSCAN algorithm, this approach enables precise identification of individual discontinuities. More importantly, the study innovatively introduces discontinuity density cloud maps. Building upon this identification methodology and integrating it with the geological strength index (GSI) for open-pit mine slopes, we analyse high-risk areas prone to rock sliding from a global perspective. This research provides effective data support for slope stability analysis in mining operations.

The strong clutter typically impedes the accurate imaging and detection of targets for through-the-wall (TWR) system. In this letter, a joint dual-domain (JDD) clutter suppression method based on robust non-negative matrix factorization (RNMF) is proposed for TWR system. The proposed method exploits RNMF algorithm to remove the clutter in both the signal and image domains. Specifically, the exponentially weighted multiplication fusion is employed to fuse the two decluttered images in the signal and image domains. The optimal value of the exponential factor for fusion is determined using the minimum entropy criterion. The experimental results have shown that the proposed method can provide the better clutter suppression performance compared to the existing low rank and sparse decomposition (LRSD) based approaches.

The accurate imaging of stationary human targets in the indoor scene containing strong scatterers such as cabinets, tables, and chairs is very important for the through-the-wall radar (TWR) system. The convolution neural network (CNN) has been used to enhance radar imaging quality. In this letter, a novel multiresolution fusion network (MRFN) based on the deep layer aggregation (DLA) method is proposed for TWR imaging. The proposed MRFN can accurately localize the weak scattering human targets and provide the scattering intensity differences between the strong and weak targets. Both the simulated and real TWR data are used to evaluate the imaging performance of the proposed MRFN. The experimental results demonstrate the superiority of the proposed TWR imaging method over the existing CNN-based imaging methods.

Despite deep convolutional neural networks (DCNNs) having been used extensively in radar-based human activity recognition in recent years, their performance could not be fully implemented because of the lack of radar dataset. However, radar data acquisition is difficult to achieve due to the high cost of its measurement. Generative adversarial networks (GANs) can be utilized to generate a large number of similar micro-Doppler signatures with which to increase the training data set. For the training of DCNNs, the quality and diversity of data set generated by GANs is particularly important. In this paper, we propose using a more stable and effective Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to augment the training data set. The classification results from the experimental data have shown the proposed method can improve the classification accuracy of human activity.

Estimating unknown wall parameters is of great importance for the application of through-the-wall radar (TWR). The time-delay-only estimation (TDOE) method is able to efficiently retrieve the constitute parameters of the homogeneous wall under test. For the TDOE method, the estimation accuracy of time delays associated with wall reflections directly affects the accuracy of wall parameters estimation. In this paper, we propose a sparse recovery method for estimation of wall parameters, which utilizes the orthogonal matching pursuit (OMP) algorithm to perform the time delay estimation of echoes backscattered from the wall at each antenna separation. Numerical simulation results have shown that the proposed estimation method is capable of providing the estimation of wall parameters with higher accuracy.

Estimating the time delays of buried target echoes is particularly important for the application of ground penetrating radar (GPR). Due to its smaller computational burden, the estimation of signal parameters via rotational invariance technique (ESPRIT) is preferred to process the buried target echoes in the frequency domain and to obtain the accurate super-resolution time delays. In this letter, we give an in-depth analysis of the essential preprocessing steps for the application of ESPRIT to practical GPR measurement data. In particular, an improved spatial smoothing method is adopted to construct the correlation matrix for the robustness of the time-delay estimation result. The effectiveness of the algorithm is verified by the synthetic data from a horizontally stratified medium model using the finite-difference time-domain method, which explicitly takes into account surface scattering for a more realistic scenario.

For stepped frequency continuous wave ground penetrating radar (SFCW GPR), the image of buried targets is usually reconstructed by a combination of point-like scatters whose number is much smaller than that of pixels of target space image. The intrinsic sparseness of target space offers a migration imaging method to make the high-quality image of underground region based on compressive sensing (CS) theory. In this paper, the effects of air/ground interface and antenna beamwidth on CS-based SFCW GPR migration imaging are presented and analyzed. It is shown that the presence of the strong air/ground interface reflection and finite antenna beamwidth usually challenges the robust CS migration imaging algorithm in practice. To overcome this problem in the context of CS migration imaging, an improved CS migration imaging method for SFCW GPR system is proposed in this paper. Experimental results show that the approach is robust to deliver a high-quality image of underground region.