In recent years, a rise in interest in using Unmanned Aerial Vehicles (UAV) with LiDAR (Light Detection and Ranging) to capture the 3D structure of forests for forestry and ecosystem monitoring applications has been witnessed. Since the terrain is an essential basis for the vertical structure modeling of a forest, the point cloud filtering delivering a highly accurate Digital Terrain Model (DTM) contributes significantly to forest studies. Conventional point cloud filtering algorithms require users to select suitable parameters according to the knowledge of the algorithm and the characteristics of scanned scenes, which are normally empirical and time-consuming. Deep learning offers a novel method in classifying and segmenting LiDAR point cloud, while there are only few studies reported on utilizing deep learning to filter non-ground LiDAR points of forested environments. In this study, we proposed an end-to-end and highly-efficient network named Terrain-net which combines the 3D point convolution operator and self-attention mechanism to capture local and global features for UAV point cloud ground filtering. The network was trained with over 15 million labeled points of 70 forest sites and was evaluated at 17 sites covering various forested environments. Terrain-net was compared with four classical filtering algorithms and one of the most well-recognized point convolution-based deep learning methods (KP-FCNN). Results indicated that Terrain-net achieved the best performance in respect of the Kappa coefficient (0.93), MIoU (0.933) and overall accuracy (98.0%). Terrain-net also performed well in transferring to an additional third-party open dataset for ground filtering in large-scale scenes and other vegetated environments. No parameters need to be tuned in transferring predictions. Terrain-net will hopefully be widely applied as a new highly-efficient, parameter-free, and easy-to-use tool for LiDAR data ground filtering in varying forest environments.

Deep learning-based detection methods have become mainstream to solve aircraft detection problems in SAR images. However, the distinct scattering characteristics displayed by SAR aircraft have been rarely explored in the context of traditional deep learning-based methods. Furthermore, almost all state-of-the-art object detectors, such as YOLO-series, Faster R-CNN, RetinaNet, rely on pre-defined anchor boxes, which may lead to complex computations and redundancy in regard to hyper-parameters. To address these issues, this letter first proposes representing aircraft in SAR images using point features that highlight key semantic areas of the target and are consistent with the scattering properties of SAR imagery. Object areas can then be bounded using the arrangement of these points, avoiding the need to explicitly calculate every anchor box. Further, an Incentive Attention Feature Fusion (IAFF) strategy is proposed to improve feature fusion efficiency and suppress the impact of redundant information on point features. Experiments conducted on four publicly available SAR aircraft detection datasets demonstrate the efficiency and effectiveness of the proposed method.

Pine wilt disease (PWD) is a highly contagious and devastating forest disease. The timely detection of pine trees infected with PWD in the early stage is of great significance to effectively control the spread of PWD and protect forest resources. However, in the spatial domain, the features of early-stage PWD are not distinctly evident, leading to numerous missed detections and false positives when directly using spatial-domain images. However, we found that frequency domain information can more clearly express the characteristics of early-stage PWD. In this paper, we propose a detection method based on deep learning for early-stage PWD by comprehensively utilizing the features in the frequency domain and the spatial domain. An attention mechanism is introduced to further enhance the frequency domain features. Employing two deformable convolutions to fuse the features in both domains, we aim to fully capture semantic and spatial information. To substantiate the proposed method, this study employs UAVs to capture images of early-stage pine trees infected with PWD at Dahuofang Experimental Forest in Fushun, Liaoning Province. A dataset of early infected pine trees affected by PWD is curated to facilitate future research on the detection of early-stage infestations in pine trees. The results on the early-stage PWD dataset indicate that, compared to Faster R-CNN, DETR and YOLOv5, the best-performing method improves the average precision (AP) by 17.7%, 6.2% and 6.0%, and the F1 scores by 14.6%, 3.9% and 5.0%, respectively. The study provides technical support for early-stage PWD tree counting and localization in the field in forest areas and lays the foundation for the early control of pine wood nematode disease.

In this study, the authors study the video annotation problem over heterogeneous domains, in which data from the image source domain and the video target domain is represented by heterogeneous features with different dimensions and physical meanings. A novel feature learning method, called heterogeneous discriminative analysis of canonical correlation (HDCC), is proposed to discover a common feature subspace in which heterogeneous features can be compared. The HDCC utilises discriminative information from the source domain as well as topology information from the target domain to learn two different projection matrices. By using these two matrices, heterogeneous data can be projected onto a common subspace and different features can be compared. They additionally design a group weighting learning framework for multi‐domain adaptation to effectively leverage knowledge learned from the source domain. Under this framework, source domain images are organised in groups according to their semantic meanings, and different weights are assigned to these groups according to their relevancies to the target domain videos. Extensive experiments on the Columbia Consumer Video and Kodak datasets demonstrate the effectiveness of their HDCC and group weighting methods.