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Light detection and ranging (LiDAR) technology has revolutionized forest analysis in past two decades. The increase of available LiDAR data volume is accelerating in recent years. However, the dense and large-volume point clouds may constitute challenges for proper data storage and processing. Point subsampling is often a prerequisite in this circumstance. Nonetheless, the commonly used uniform and random subsampling methods fail to preserve the topological details of branching structures, as they essentially drop points globally. This generates problems for studies on detailed branching structures, and currently there are no point subsampling methods designed for trees. In this letter, a structure-aware subsampling (SAS) method is proposed to tackle this issue. SAS relies on skeleton-adaptive clustering to subsample points locally, and maintain the global integrity simultaneously. The proposed method was tested and compared with uniform and random subsampling for retrieving key tree parameters including height, diameter at breast height (DBH), crown area, and wood volume based on geometrical reconstructions. Three datasets from terrestrial, mobile, and unmanned aerial vehicles (UAV) LiDAR platforms were tested. Results showed that SAS was able to achieve similar accuracies of structural parameters compared to the full-resolution data, even with a subsampling rate of over 90%. More importantly, at the same sampling rate, SAS faithfully preserved more points of thin branches compared to uniform and random subsampling. These results imply that the proposed method maintains the complex tree topology while significantly reduces the data size. This study provides a crucial advancement in LiDAR and forest applications, where data reduction still remains widely unexplored.

Accurate 2-D frequency spectrum (2-D FS) with acceleration, two-way range coupling terms, and spatial-variant Doppler parameters are the main problems to be solved in translational-variant bistatic forward-looking synthetic aperture radar (SAR) (TV BFSAR) with curved trajectory. For these issues, a modified omega-K imaging algorithm is derived in this article. The maximum usage of 2-D FS based on the method of series reversion (MSR) is achieved by linear range cell migration correction, and 2-D FS is linearized in bistatic range by using high-order polynomial fitting. Then, a method of azimuth resampling is introduced to implement compensation of spatial-variant Doppler parameters. Different from other bistatic omega-K methods, our newly proposed method focuses on the small-aperture data in the azimuth frequency domain to avoid azimuth aliasing without padding zeros and uses the frequency focusing position to study the model of spatial-variant phase. Simulation results and real data verify the effectiveness of the proposed method.

With the development of new active deception jamming, radar anti-jamming has become a major research hotspot, and the recognition of jamming type is one of its key steps. In recent years, deep learning has been successfully applied in the field of radar jamming recognition, such as convolutional neural networks (CNN). However, it is difficult to effectively improve the accuracy of deep learning algorithms in the case of small sample. Furthermore, ensemble learning and transfer learning can effectively improve the model generalization performance. For the small sample problem, this article proposes a weighted ensemble CNN with transfer learning (WECNN-TL)-based radar active deception jamming recognition algorithm. The main idea of this method is to obtain the time-frequency distribution maps of jamming signals by short-time Fourier transform (STFT), and then, their real parts, imaginary parts, moduli, and phases are combined differently to construct multiple datasets. Finally, an ensemble CNN model with weighted voting and transfer learning is constructed to realize jamming recognition. Experiments on the simulated and measured mixed dataset (including 12 types of samples) show that the proposed method can get better recognition performance than random forest (RF), support vector machine (SVM), and some CNN-based methods.