The complexity of indoor point cloud and information loss during point cloud acquisition are the main challenges for accurate and complete indoor buildings 3D wireframe construction. This paper presents a 3D wireframe construction method for indoor structure point clouds based on Gestalt rules. First, boundary points of indoor structures are used for structural plane segmentation, including ceilings, floors, walls, beams, etc. Then, point clouds of each structural plane are projected onto its best-fit plane to obtain a 2D projection, followed by boundary points extraction and line fitting to get the 2D line segments. Finally, an accurate and complete 3D wireframe is constructed by optimizing anomalous line segments based on the Gestalt rules. In our method, Gestalt rules are used to deal with problems in line segments, including cluttered distribution, duplicate extraction, non-regularization (non-parallel and non-perpendicular), topological errors (non-closed and discontinuous), and missing information. Experiments demonstrate that our proposed method significantly improves the accuracy and completeness of the results.
Water leakage poses a significant threat to the safe operation of tunnels. We utilized a Mobile Laser Scanner (MLS) to collect point cloud data under adverse tunnel conditions. A data mapping approach was employed to generate MLS point cloud intensity images. Tailored for multiscale point cloud intensity images, we devised a lightweight object detection network to identify areas affected by water leakage promptly. Integrating efficient receptive field expansion convolution (EREC) into lightweight network models facilitated efficient feature extraction. Additionally, we designed an effective attention-inducing downsampling unit (EADU) to construct a tunnel leakage detection model. This module comprehensively handles target features, enhances target context information, enlarges the receptive field, and establishes a unique information processing framework for detecting various multisize targets, achieving outstanding detection performance. Moreover, we developed a dynamic threshold adaptive loss function that automatically adjusts the loss function based on leakage detection performance to enhance the model's ability to detect challenging targets. Finally, we employed a twin attention-guided dynamic detection-head (TADD) to improve detection performance. Experimental results demonstrate that our method effectively transforms the process from MLS point cloud data acquisition to high-precision target detection. The leakage detection network has achieved an optimal balance between efficiency and accuracy, surpassing comparative methods, thereby ensuring the secure operation of shield tunnels.
Deep learning approaches have been widely used in building automatic extraction tasks and have made great progress in recent years. However, the missing detection and wrong detection causing by spectrum confusion is still a great challenge. The existing fully convolutional networks (FCNs) cannot effectively distinguish whether the feature differences are from one building or the building and its adjacent non-building objects. In order to overcome the limitations, a building multi-feature fusion refined network (BMFR-Net) was presented in this paper to extract buildings accurately and completely. BMFR-Net is based on an encoding and decoding structure, mainly consisting of two parts: the continuous atrous convolution pyramid (CACP) module and the multiscale output fusion constraint (MOFC) structure. The CACP module is positioned at the end of the contracting path and it effectively minimizes the loss of effective information in multiscale feature extraction and fusion by using parallel continuous small-scale atrous convolution. To improve the ability to aggregate semantic information from the context, the MOFC structure performs predictive output at each stage of the expanding path and integrates the results into the network. Furthermore, the multilevel joint weighted loss function effectively updates parameters well away from the output layer, enhancing the learning capacity of the network for low-level abstract features. The experimental results demonstrate that the proposed BMFR-Net outperforms the other five state-of-the-art approaches in both visual interpretation and quantitative evaluation.
The joint utilization of multi-source data is of great significance in geospatial observation applications, such as urban planning, disaster assessment, and military applications. However, this approach is confronted with challenges including inconsistent data structures, irrelevant physical properties, scarce training data, insufficient utilization of information and an imperfect feature fusion method. Therefore, this paper proposes a novel binary-tree Transformer network (BTRF-Net), which is used to fuse heterogeneous information and utilize complementarity among multi-source remote sensing data to enhance the joint classification performance of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. Firstly, a hyperspectral network (HSI-Net) is employed to extract spectral and spatial features of hyperspectral images, while the elevation information of LiDAR data is extracted using the LiDAR network (LiDAR-Net). Secondly, a multi-source transformer complementor (MSTC) is designed that utilizes the complementarity and cooperation among multi-modal feature information in remote sensing images to better capture their correlation. The multi-head complementarity attention mechanism (MHCA) within this complementor can effectively capture global features and local texture information of images, hence achieving full feature fusion. Then, to fully obtain feature information of multi-source remote sensing images, this paper designs a complete binary tree structure, binary feature search tree (BFST), which fuses multi-modal features at different network levels to obtain multiple image features with stronger representation abilities, effectively enhancing the stability and robustness of the network. Finally, several groups of experiments are designed to compare and analyze the proposed BTRF-Net with traditional methods and several advanced deep learning networks using two datasets: Houston and Trento. The results show that the proposed network outperforms other state-of-the-art methods even with small training samples.
Accurate semantic segmentation results of the overhead catenary system (OCS) are significant for OCS component extraction and geometric parameter detection. Actually, the scenes of OCS are complex, and the density of point cloud data obtained through Light Detection and Ranging (LiDAR) scanning is uneven due to the character difference of OCS components. However, due to the inconsistent component points, it is challenging to complete better semantic segmentation of the OCS point cloud with the existing deep learning methods. Therefore, this paper proposes a point cloud multi-scale feature fusion refinement structure neural network (PMFR-Net) for semantic segmentation of the OCS point cloud. The PMFR-Net includes a prediction module and a refinement module. The innovations of the prediction module include the double efficient channel attention module (DECA) and the serial hybrid domain attention (SHDA) structure. The point cloud refinement module (PCRM) is used as the refinement module of the network. DECA focuses on detail features; SHDA strengthens the connection of contextual semantic information; PCRM further refines the segmentation results of the prediction module. In addition, this paper created and released a new dataset of the OCS point cloud. Based on this dataset, the overall accuracy (OA), F1-score, and mean intersection over union (MIoU) of PMFR-Net reached 95.77%, 93.24%, and 87.62%, respectively. Compared with four state-of-the-art (SOTA) point cloud deep learning methods, the comparative experimental results showed that PMFR-Net achieved the highest accuracy and the shortest training time. At the same time, PMFR-Net segmentation performance on S3DIS public dataset is better than the other four SOTA segmentation methods. In addition, the effectiveness of DECA, SHDA structure, and PCRM was verified in the ablation experiment. The experimental results show that this network could be applied to practical applications.
Due to the external environment, the support positioning device (SPD) of the railway catenary is prone to break down. Therefore, the automatic detection of SPDs is of great research significance to ensure the safe operation of the railway system. However, the small-scale SPD expresses weak features due to the complexity of railway scenes. Therefore, we propose a new algorithm for automatically extracting multiple-type SPDs quickly. Firstly, a preprocessing approach of equidistant scene segmentation is proposed based on trajectory points to divide the whole scene into several independent region units for batch processing. Then, a new point spacing-based spatial clustering of applications with noise (PSBSCAN) algorithm is proposed for the extraction of pillars in the pillar corridor of each region unit. Adopting point spacing as the core standard and the parallel iteration mode with only accessing edge points can reduce the influence of the uneven point cloud density and the large-scale point clouds. Next, to locate SPDs, the spatial relationship between the trajectory point, the pillar, and the SPD in the scene is introduced into the corresponding objects to build the spatial index with semantic information. Finally, contact wires installed on the SPD are filtered through multiple-level voxel segmentation based on octree to extract SPD accurately. Through the test of 10km railway datasets, the average F 1 by the proposed method is 98.61%, and the average recognition rate of SPD is 99.65%, which shows that this method can achieve efficient extraction of multiple-type SPDs in railway scenes with different point densities.
Due to the inherent limitations in remote sensing image quality, seasonal variations, and radiometric inconsistencies, river extraction based on remote sensing image classification often results in omissions. These challenges are particularly pronounced in the detection of narrow and complex river networks, where fine river features are frequently underrepresented, leading to fragmented and discontinuous water body extraction. To address these issues and enhance both the completeness and accuracy of fine river identification, this study proposes an advanced fine river extraction and optimization method. Firstly, a linear river feature enhancement algorithm for preliminary optimization is introduced, which combines Frangi filtering with an improved GA-OTSU segmentation technique. By thoroughly analyzing the global features of high-resolution remote sensing images, Frangi filtering is employed to enhance the river linear characteristics. Subsequently, the improved GA-OTSU thresholding algorithm is applied for feature segmentation, yielding the initial results. In the next stage, to preserve the original river topology and ensure stripe continuity, a river skeleton refinement algorithm is utilized to retain critical skeletal information about the river networks. Following this, river endpoints are identified using a connectivity domain labeling algorithm, and the bounding rectangles of potential disconnected regions are delineated. To address discontinuities, river endpoints are shifted and reconnected based on structural similarity index (SSIM) metrics, effectively bridging gaps in the river network. Finally, nonlinear water optimization combined K-means clustering segmentation, topology and spectral inspection, and small-area removal are designed to supplement some missed water bodies and remove some non-water bodies. Experimental results demonstrate that the proposed method significantly improves the regularization and completeness of river extraction, particularly in cases of fine, narrow, and discontinuous river features. The approach ensures more reliable and consistent river delineation, making the extracted results more robust and applicable for practical hydrological and environmental analyses.
Building boundary optimization is an essential post-process step for building extraction (by image classification). However, current boundary optimization methods through smoothing or line fitting principles are unable to optimize complex buildings. In response to this limitation, this paper proposes an object-oriented building contour optimization method via an improved generalized gradient vector flow (GGVF) snake model and based on the initial building contour results obtained by a classification method. First, to reduce interference from the adjacent non-building object, each building object is clipped via their extended minimum bounding rectangles (MBR). Second, an adaptive threshold Canny edge detection is applied to each building image to detect the edges, and the progressive probabilistic Hough transform (PPHT) is applied to the edge result to extract the line segments. For those cases with missing or wrong line segments in some edges, a hierarchical line segments reconstruction method is designed to obtain complete contour constraint segments. Third, accurate contour constraint segments for the GGVF snake model are designed to quickly find the target contour. With the help of the initial contour and constraint edge map for GGVF, a GGVF force field computation is executed, and the related optimization principle can be applied to complex buildings. Experimental results validate the robustness and effectiveness of the proposed method, whose contour optimization has higher accuracy and comprehensive value compared with that of the reference methods. This method can be used for effective post-processing to strengthen the accuracy of building extraction results.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)