Individual tree segmentation from point clouds is essential for diverse forest applications. A dual-branch segmentation deep learning network operating at the grid scale was proposed, which includes the semantic segmentation branch for partitioning point clouds of tree trunks and the instance segmentation branch for individual trunk extraction. Meanwhile, the network analyzes input forest points at the grid scale instead of pointwise processing to preserve local geometric information of the forest points while reducing computational load. After extraction of each tree trunk in the understory layer using our network, a hierarchical k-nearest neighbors algorithm based on the extracted trunk parts was employed to accomplish individual tree segmentation. For the forest plots, our proposed approach achieves precision, recall,${F}1$-score, and mean intersection over union (MIoU) of 89.66%, 89.13%, 89.40%, and 90.84%, respectively. These results represent a significant improvement in accuracy and rapid execution capability compared to prior methods.

Dynamic functional brain network (DFBN) can flexibly describe the time-varying topological connectivity patterns of the brain, and show great potential in brain disease diagnosis. However, most of the existing DFBN analysis methods focus on capturing the dynamic interaction at the brain region level, ignoring the spatio-temporal topological evolution across time windows. Moreover, they are difficult to suppress interfering connections in DFBNs, which leads to a diminished capacity for discerning the intrinsic structures that are intimately linked to brain disorders. To address these issues, we propose a topological evolution graph learning model to capture disease-related spatio-temporal topological features in DFBNs. Specifically, we first take the hubness of adjacent DFBN as the source domain and the target domain in turn, and then use Wasserstein distance (WD) and Gromov-Wasserstein distance (GWD) to capture the brain's evolution law at the node and edge levels, respectively. Furthermore, we introduce the principle of relevant information to guide the topology evolution graph to learn the structures that are most relevant to brain diseases yet least redundant information between adjacent DFBNs. On this basis, we develop a high-order spatio-temporal model with multi-hop graph convolution to collaboratively extract long-range spatial and temporal dependencies from the topological evolution graph. Extensive experiments show that the proposed method outperforms the current state-of-the-art methods, and can effectively reveal the information evolution mechanism between brain regions across windows.

Abstract In order to balance accuracy and real‐time performance in semantic segmentation, this paper proposes a real‐time semantic segmentation algorithm model based on attention mechanism and multi‐branch feature fusion using Fast convolutional neural network model (Fast‐SCNN). In this method, the spatial detail feature enhancement branch is introduced to enhance spatial detail features firstly. Then, through rational design of fusion module, the feature information of each branch is optimized to achieve better fusion of deep and shallow features. At the end of the feature fusion module, an adaptive feature enhancement focus module is introduced to capture the interdependence between remote pixels. The experimental results show that the proposed algorithm achieves 71.55% segmentation accuracy on Cityscapes dataset, the reasoning speed FPS is 97.6 frames/s, and the number of parameters is 1.39 M, which verifies the effectiveness of the network model constructed by the algorithm. Code is available at https://github.com/ccchhheeennn/model .

Clouds in satellite images limit the ability of imagery to extract the ground information, which makes it difficult to the following image analysis tasks. Hence, cloud detection is a changeling but fundamental task in the preprocessing of satellite image processing. In this letter, an encoder–decoder neural network architecture, cloud detection with ACON and attention mechanism (CAA)-UNet, is proposed for cloud detection. CAA-UNet is based on U-Net architecture and incorporates the attention mechanism. The asymmetric encoder and decoder blocks are proposed to discover more discriminative features. Then, a modified attention gate is integrated into each skip connection to highlight salient features. These make our model distinguish between the cloud and noncloud more accurately. Furthermore, the recent new and effective activate function, ActivateOrNot (ACON), is introduced into our model, which allows each neuron to adaptively activate or not, and improves the performance remarkably. Finally, the experiment results on two cloud datasets, Landsat-8 and a high-resolution cloud (HRC) cover validation dataset, show that CAA-UNet outperforms the state-of-the-art methods, especially in comprehensive indicators: Jaccard index,$F_{1}$score, and overall accuracy.