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Although many scholars have realized that deep learning methods have great advantages in hyperspectral lithology classification now, most of them use simple convolutional neural networks for discussion, which are difficult to effectively extract spectral sequence information from hyperspectral image (HSI). To address this issue, we propose a double-branch multiscale dual-attention (DBMSDA) network using two HSIs multiple different rock types as data sources. This network utilizes the multiscale spectral residual self-attention structure and dense connections in the spectral branch to extract diagnostic multiscale spectral information from HSIs, improving the ability to reuse spectral information. In the spatial branch, dense connections are used to fully extract diagnostic spatial information, and finally, the two are fused for classification. We compare the DBMSDA network with several traditional machine learning and deep learning methods. Experimental results show that the DBMSDA network has the best lithology classification performance and robustness. Its OA on the ZCD1 and HCD2 datasets is 85.08% and 90.80%, respectively, representing an improvement of 5.95% and 2.4% over the lowest OA, confirming that the DBMSDA network can effectively improve accuracy and is superior to other methods. Meanwhile, the experiment also showed that the DBMSDA network has good classification performance on HSIs of various rock types and HSIs from different data sources. Finally, ablation experiments were conducted to demonstrate that the DBMSDA network relies more on large training samples compared with traditional machine learning models, and that the DBMSDA network has better applicability and stability under the same sufficient training samples.

Existing algorithms based on remote sensing for landslide displacement estimation, such as C2C, C2M, DOD, and M3C2, are sensitive to errors generated in data processing, and further improving their accuracy is difficult. To address this issue, given that redundant observations may occur in landslide monitoring, we proposed a robust estimation method of landslide displacement from multitemporal unmanned aerial vehicle (UAV) photogrammetry-derived point clouds. The proposed method first establishes the relevant graph to manage the trajectory of error propagation for landslide displacement estimation for all possible paths. Two modules, namely intra- and inter-estimates, are explored to reduce the impact of outliers and high surface roughness in point clouds derived by UAV photogrammetry. Individually, the intraestimate operation is used to calculate landslide displacement between two temporal point clouds by robust estimation considering outlier constraint, and the interestimate operation is used to obtain the optimal calculation of landslide displacement by minimizing a given objective function using IGG robust estimation proposed by the Institute of Geodesy and Geophysics at the Chinese Academy of Sciences. Experimental results show that the proposed method is significantly superior to conventional methods, such as C2C, C2M, and M3C2, with an accuracy improvement of at least 8%.

Three-dimensional indoor models have wide applications in fields such as indoor navigation, civil engineering, virtual reality, and so on. With the development of LiDAR technology, automatic reconstruction of indoor models from point clouds has gained significant attention. We propose a new framework for generating indoor 3D digital models from point clouds. The proposed method first generates a room instance map of an indoor scene. Walls are detected and projected onto a horizontal plane to form line segments. These segments are extended, intersected, and, by solving an integer programming problem, line segments are selected to create room polygons. The polygons are converted into a raster image, and image connectivity detection is used to generate a room instance map. Then the roofs of the point cloud are extracted and used to perform an overlap analysis with the generated room instance map to segment the entire roof point cloud, obtaining the roof for each room. Room boundaries are defined by extracting and regularizing the roof point cloud boundaries. Finally, by detecting doors and windows in the scene in two steps, we generate the floor plans and 3D models separately. Experiments with the Giblayout dataset show that our method is robust to clutter and furniture point clouds, achieving high-accuracy models that match real scenes. The mean precision and recall for the floorplans are both 0.93, and the Point–Surface Distance (PSD) and standard deviation of the PSD for the 3D models are 0.044 m and 0.066 m, respectively.

Abstract The development of atherosclerosis (AS) is closely linked to changes in the plaque microenvironment, which consists primarily of the cells that form plaque and the associated factors they secrete. The onset of inflammation, lipid deposition, and various pathological changes in cellular metabolism that accompany the plaque microenvironment will promote the development of AS. Numerous studies have shown that oxidative stress is an important condition that promotes AS. The accumulation of reactive oxygen species (ROS) is oxidative stress’s most important pathological change. In turn, the effects of ROS on the plaque microenvironment are complex and varied, and these effects are ultimately reflected in the promotion or inhibition of AS. This article reviews the effects of ROS on the microenvironment of atherosclerotic plaques and their impact on disease progression over the past five years and focuses on the progress of treatment strategies based on scavenging ROS of nanoparticles for AS. Finally, we also discuss the prospects and challenges of AS treatment.

Background: The progression and metastasis of tumors are typically accompanied by angiogenesis. Crucially, vascular endothelial growth factor (VEGF) and its receptors (VEGFRs) play a significant role in tumor-associated angiogenesis. In this study, the aim was to investigate the antitumor effect of combining bevacizumab (Bev) with anlotinib (An) on colorectal cancer (CRC). Methods: The CCK-8 assay, EdU assay, and Annexin V staining were conducted to evaluate the proliferation and apoptosis of CRC cells in vitro. The migration capability of CRC cells and HUVECs was assessed using the Transwell assay. Additionally, the tube formation capability of HUVECs was investigated. Furthermore, the antitumor and antiangiogenic effects were evaluated in the BALB/c mice model using immunohistochemistry, TUNEL staining, and 18F-FDG PET/CT imaging. Finally, we analyzed the inhibitory effect of Bev and/or An on related signaling effectors through western blotting. Results: The in vivo CRC mice model revealed that the combination of Bev + An significantly suppressed tumor formation and angiogenesis. Bev + An inhibited tumor glucose metabolism and increased the median survival period in tumor-bearing mice. Mechanistically, the expressions of VEGF, VEGFR2, PDGFR, and FGFR, as well as the phosphorylation levels of AKT, were inhibited after Bev+An treatment. In conclusion, the dual vertical targeting of VEGF and VEGFR in the CRC mice model strongly inhibited tumor growth and angiogenesis, with the suppression of the AKT signaling pathway playing a partial role.

Although extensive research shows that CNNs achieve good classification results in HSI classification, they still struggle to effectively extract spectral sequence information from HSIs. Additionally, the high-dimensional features of HSIs, the limited number of labeled samples, and the common sample imbalance significantly restrict classification performance improvement. To address these issues, this article proposes a double-branch multi-scale dual-attention (DBMSDA) network that fully extracts spectral and spatial information from HSIs and fuses them for classification. The designed multi-scale spectral residual self-attention (MSeRA), as a fundamental component of dense connections, can fully extract high-dimensional and intricate spectral information from HSIs, even with limited labeled samples and imbalanced distributions. Additionally, this article adopts a dataset partitioning strategy to prevent information leakage. Finally, this article introduces a hyperspectral geological lithology dataset to evaluate the accuracy and applicability of deep learning methods in geology. Experimental results on the geological lithology hyperspectral dataset and three other public datasets demonstrate that the DBMSDA method exhibits superior classification performance and robust generalization ability compared to existing methods.

Insulator extraction from images or 3D point clouds is an important part of automatic power inspection by unmanned airborne vehicles (UAVs), which is vital for improving the efficiency of inspection and the stability of power grids. However, for point cloud data, many challenges, such as the diversity of pylon shape and insulator type, complex topology, and similarity of structures, were not tackled with the study of power element extraction. To efficiently identify the small insulators from complex power transmission corridor (PTC) scenarios, this paper proposes a robust extraction method by fusing multi-scale neighborhood and multi-feature entropy weighting. The pylon head is segmented according to the aspect ratio of horizontal slices following the locating of the pylons based on the height difference and continuous vertical distribution firstly. Aiming to quantify the different contributions of features in decision-making and better segment insulators, a feature evaluation system combined with information entropy, eigen entropy-based optimal neighborhood selection, and designed multi-scale features is constructed to identify suspension insulators and tension insulators. In the optimization step, a region erosion and growing method is proposed to segment complete insulator strings by enlarging the perspectives to obtain more object representations. The extraction results of 82 pylons with 654 insulators demonstrate that the proposed method is suitable for different pylon shapes and sizes. The identification accuracy of the whole line achieves 98.23% and the average F1 score is 90.98%. The proposed method can provide technical support for automatic UAV inspection and pylon reconstruction.

Landcover classification is an important application in remote sensing, but it is always a challenge to distinguish different features with similar characteristics or large-scale differences. Some deep learning networks, such as UperNet, PSPNet, and DANet, use pyramid pooling and attention mechanisms to improve their abilities in multi-scale features extraction. However, due to the neglect of low-level features contained in the underlying network and the information differences between feature maps, it is difficult to identify small-scale objects. Thus, we propose a novel image segmentation network, named HFENet, for mining multi-level semantic information. Like the UperNet, HFENet adopts a top-down horizontal connection architecture while includes two improved modules, the HFE and the MFF. According to the characteristics of different levels of semantic information, HFE module reconstructs the feature extraction part by introducing an attention mechanism and pyramid pooling module to fully mine semantic information. With the help of a channel attention mechanism, MFF module up-samples and re-weights the feature maps to fuse them and enhance the expression ability of multi-scale features. Ablation studies and comparative experiments between HFENet and seven state-of-the-art models (U-Net, DeepLabv3+, PSPNet, FCN, UperNet, DANet and SegNet) are conducted with a self-labeled GF-2 remote sensing image dataset (MZData) and two open datasets landcover.ai and WHU building dataset. The results show that HFENet on three datasets with six evaluation metrics (mIoU, FWIoU, PA, mP, mRecall and mF1) are better than the other models and the mIoU is improved 7.41–10.60% on MZData, 1.17–11.57% on WHU building dataset and 0.93–4.31% on landcover.ai. HFENet can perform better in the task of refining the semantic segmentation of remote sensing images.

With the development of deep learning algorithms, more and more deep learning algorithms are being applied to remote sensing image classification, detection, and semantic segmentation. The landslide semantic segmentation of a remote sensing image based on deep learning mainly uses supervised learning, the accuracy of which depends on a large number of training data and high-quality data annotation. At this stage, high-quality data annotation often requires the investment of significant human effort. Therefore, the high cost of remote sensing landslide image data annotation greatly restricts the development of a landslide semantic segmentation algorithm. Aiming to resolve the problem of the high labeling cost of landslide semantic segmentation with a supervised learning method, we proposed a remote sensing landslide semantic segmentation with weakly supervised learning method combing class activation maps (CAMs) and cycle generative adversarial network (cycleGAN). In this method, we used the image level annotation data to replace pixel level annotation data as the training data. Firstly, the CAM method was used to determine the approximate position of the landslide area. Then, the cycleGAN method was used to generate the fake image without a landslide, and to make the difference with the real image to obtain the accurate segmentation of the landslide area. Finally, the pixel-level segmentation of the landslide area on remote sensing image was realized. We used mean intersection-over-union (mIOU) to evaluate the proposed method, and compared it with the method based on CAM, whose mIOU was 0.157, and we obtain better result with mIOU 0.237 on the same test dataset. Furthermore, we made a comparative experiment using the supervised learning method of a u-net network, and the mIOU result was 0.408. The experimental results show that it is feasible to realize landslide semantic segmentation in a remote sensing image by using weakly supervised learning. This method can greatly reduce the workload of data annotation.

Most traditional methods have difficulty detecting landslide boundary accurately, and the existing methods based on deep learning often lead to insufficient training or overfitting due to insufficient samples. An end-to-end, semi-supervised adversarial network, which fully considers spectral and topographic features derived using unmanned aerial vehicle (UAV) photogrammetry, is proposed to extract landslides by semantic segmentation to address the abovementioned problem. In the generative network, a generator similar to pix2pix is introduced into the proposed adversarial nets to learn semantic features from UAV-photogrammetry-derived data by semi-supervised operation and a confrontational strategy to reduce the requirement of the number of labeled samples. In the discriminative network, DeepLabv3+ is improved by inserting multilevel skip connection architecture with upsampling operation to obtain the contextual information and retain the boundary information of landslides at all levels, and a topographic convolutional neural network is proposed to be inserted into the encoder to concatenate topographic features together with spectral features. Then, transfer learning with the pre-trained parameters and weights, shared with pix2pix and DeepLabv3+, is used to perform landslide extraction training and validation. In our experiments, the UAV-photogrammetry-derived data of a typical landslide located at Meilong gully in China are collected to test the proposed method. The experimental results show that our method can accurately detect the area of a landslide and achieve satisfactiory results based on several indicators including the Precision, Recall, F1 score, and mIoU, which are 13.07%, 15.65%, 16.96%, and 18.23% higher than those of the DeepLabV3+. Compared with state-of-the-art methods such as U-Net, PSPNet, and pix2pix, the proposed adversarial nets considering multidimensional information such as topographic factors can perform better and significantly improve the accuracy of landslide extraction.

Power-line inspection is an important means to maintain the safety of power networks. Light detection and ranging (LiDAR) technology can provide high-precision 3D information about power corridors for automated power-line inspection, so there are more and more utility companies relying on LiDAR systems instead of traditional manual operation. However, it is still a challenge to automatically detect power lines with high precision. To achieve efficient and accurate power-line extraction, this paper proposes an algorithm using entropy-weighting feature evaluation (EWFE), which is different from the existing hierarchical-multiple-rule evaluation of many geometric features. Six significant features are selected (Height above Ground Surface (HGS), Vertical Range Ratio (VRR), Horizontal Angle (HA), Surface Variation (SV), Linearity (LI) and Curvature Change (CC)), and then the features are combined to construct a vector for quantitative evaluation. The feature weights are determined by an entropy-weighting method (EWM) to achieve optimal distribution. The point clouds are filtered out by the HGS feature, which possesses the highest entropy value, and a portion of non-power-line points can be removed without loss of power-line points. The power lines are extracted by evaluation of the other five features. To decrease the interference from pylon points, this paper analyzes performance in different pylon situations and performs an adaptive weight transformation. We evaluate the EWFE method using four datasets with different transmission voltage scales captured by a light unmanned aerial vehicle (UAV) LiDAR system and a mobile LiDAR system. Experimental results show that our method demonstrates efficient performance, while algorithm parameters remain consistent for the four datasets. The precision F value ranges from 98.4% to 99.7%, and the efficiency ranges from 0.9 million points/s to 5.2 million points/s.

Road markings that provide instructions for unmanned driving are important elements in high-precision maps. In road information collection technology, multi-beam mobile LiDAR scanning (MLS) is currently adopted instead of traditional mono-beam LiDAR scanning because of the advantages of low cost and multiple fields of view for multi-beam laser scanners; however, the intensity information scanned by multi-beam systems is noisy and current methods designed for road marking detection from mono-beam point clouds are of low accuracy. This paper presents an accurate algorithm for detecting road markings from noisy point clouds, where most nonroad points are removed and the remaining points are organized into a set of consecutive pseudo-scan lines for parallel and/or online processing. The road surface is precisely extracted by a moving fitting window filter from each pseudo-scan line, and a marker edge detector combining an intensity gradient with an intensity statistics histogram is presented for road marking detection. Quantitative results indicate that the proposed method achieves average recall, precision, and Matthews correlation coefficient (MCC) levels of 90%, 95%, and 92%, respectively, showing excellent performance for road marking detection from multi-beam scanning point clouds.