An Object- and Topology-Based Analysis (OTBA) Method for Mapping Rice-Crayfish Fields in South China

The rice-crayfish field (i.e., RCF), a newly emerging rice cultivation pattern, has greatly expanded in China in the last decade due to its significant ecological and economic benefits. The spatial distribution of RCFs is an important dataset for crop planting pattern adjustment, water resource management and yield estimation. Here, an object- and topology-based analysis (OTBA) method, which considers spectral-spatial features and the topological relationship between paddy fields and their enclosed ditches, was proposed to identify RCFs. First, we employed an object-based method to extract crayfish breeding ditches using very high-resolution images. Subsequently, the paddy fields that provide fodder for crayfish were identified according to the topological relationship between the paddy field and circumjacent crayfish ditch. The extracted ditch objects together with those paddy fields were merged to derive the final RCFs. The performance of the OTBA method was carefully evaluated using the RCF and non-RCF samples. Moreover, the effects of different spatial resolutions, spectral bands and temporal information on RCF identification were comprehensively investigated. Our results suggest the OTBA method performed well in extracting RCFs, with an overall accuracy of 91.77%. Although the mapping accuracies decreased as the image spatial resolution decreased, satisfactory RCF mapping results (>80%) can be achieved at spatial resolutions greater than 2 m. Additionally, we demonstrated that the mapping accuracy can be improved by more than 10% when near-infrared (NIR) band information was involved, indicating the necessity of the NIR band when selecting images to derive reliable RCF maps. Furthermore, the images acquired in the rice growth phase are recommended to maximize the differences of spectral characteristics between paddy fields and ditches. These promising findings suggest that the OTBA approach performs well for mapping RCFs in areas with fragmented agricultural landscapes, which provides fundamental information for further agricultural land use and water resources management.

Im2Mesh GAN: Accurate 3D Hand Mesh Recovery from a Single RGB Image

This work addresses hand mesh recovery from a single RGB image. In contrast to most of the existing approaches where parametric hand models are employed as the prior, we show that the hand mesh can be learned directly from the input image. We propose a new type of GAN called Im2Mesh GAN to learn the mesh through end-to-end adversarial training. By interpreting the mesh as a graph, our model is able to capture the topological relationship among the mesh vertices. We also introduce a 3D surface descriptor into the GAN architecture to further capture the associated 3D features. We conduct experiments with the proposed Im2Mesh GAN architecture in two settings: one where we can reap the benefits of coupled groundtruth data availability of the images and the corresponding meshes; and the other which combats the more challenging problem of mesh estimation without the corresponding groundtruth. Through extensive evaluations we demonstrate that even without using any hand priors the proposed method performs on par or better than the state-of-the-art.

Adaptive Machining Scheme for Multi-Hole Part with Multi-Position Accuracy Requirements

The machining of multi-hole parts often has complex correlated position accuracy requirements. When some position accuracies do not meet the requirements, several hole axes need to be adjusted. Previous methods usually correct all deviated axes to their theoretical locations. However, the correction workload is too large and inefficient. This paper proposes an efficient and adaptive hole position correction model for multi-hole part. First, the method establishes the topological relationship of the holes and faces on the part according to the position accuracy requirements of the multi-hole part. Then, the goal is to minimize the number of holes that need to be corrected. In this model, the parallelism of holes, perpendicularity, and other constraints are considered. The simulation and experimental results show that the use of this model can effectively reduce the number of holes that need to be corrected during the compensation of the position error between holes. It improves the efficiency in the subsequent compensation process significantly.

Automatic Landform Recognition from the Perspective of Watershed Spatial Structure Based on Digital Elevation Models

Landform recognition is one of the most significant aspects of geomorphology research, which is the essential tool for landform classification and understanding geomorphological processes. Watershed object-based landform recognition is a new spot in the field of landform recognition. However, in the relevant studies, the quantitative description of the watershed generally focused on the overall terrain features of the watershed, which ignored the spatial structure and topological relationship, and internal mechanism of the watershed. For the first time, we proposed an effective landform recognition method from the perspective of the watershed spatial structure, which is separated from the previous studies that invariably used terrain indices or texture derivatives. The slope spectrum method was used herein to solve the uncertainty issue of the determination on the watershed area. Complex network and P–N terrain, which are two effective methodologies to describe the spatial structure and topological relationship of the watershed, were adopted to simulate the spatial structure of the watershed. Then, 13 quantitative indices were, respectively, derived from two kinds of watershed spatial structures. With an advanced machine learning algorithm (LightGBM), experiment results showed that the proposed method showed good comprehensive performances. The overall accuracy achieved 91.67% and the Kappa coefficient achieved 0.90. By comparing with the landform recognition using terrain indices or texture derivatives, it showed better performance and robustness. It was noted that, in terms of loess ridge and loess hill, the proposed method can achieve higher accuracy, which may indicate that the proposed method is more effective than the previous methods in alleviating the confusion of the landforms whose morphologies are complex and similar. In addition, the LightGBM is more suitable for the proposed method, since the comprehensive manifestation of their combination is better than other machine learning methods by contrast. Overall, the proposed method is out of the previous landform recognition method and provided new insights for the field of landform recognition; experiments show the new method is an effective and valuable landform recognition method with great potential as well as being more suitable for watershed object-based landform recognition.

Automatic Reconstruction of Building Façade Model from Photogrammetric Mesh Model

Three-dimensional (3D) building façade model reconstruction is of great significance in urban applications and real-world visualization. This paper presents a newly developed method for automatically generating a 3D regular building façade model from the photogrammetric mesh model. To this end, the contour is tracked on irregular triangulation, and then the local contour tree method based on the topological relationship is employed to represent the topological structure of the photogrammetric mesh model. Subsequently, the segmented contour groups are found by analyzing the topological relationship of the contours, and the original mesh model is divided into various components from bottom to top through the iteration process. After that, each component is iteratively and robustly abstracted into cuboids. Finally, the parameters of each cuboid are adjusted to be close to the original mesh model, and a lightweight polygonal mesh model is taken from the adjusted cuboid. Typical buildings and a whole scene of photogrammetric mesh models are exploited to assess the proposed method quantitatively and qualitatively. The obtained results reveal that the proposed method can derive a regular façade model from a photogrammetric mesh model with a certain accuracy.

Research on Recognition of Topological Relations Between Residential Lines in Low Voltage Station Area Based on Correlation Analysis Algorithm and Probabilistic Decision Method

In order to solve the problems of low accuracy and incomprehensive recognition of the topological relationship between households in the station area and the incomplete recognition results in traditional methods, a method for identifying topological relationships between household changes in low-voltage stations based on correlation analysis algorithm and probabilistic decision method is proposed. The BIRCH method is used to cluster the topological relationship characteristics of the household line changes in the low-voltage station area, and the topological relationship characteristics are obtained through clustering parameter initialization, clustering implementation and clustering evaluation, and the user phases in the topological relationship are identified according to the feature clustering results. The correlation analysis method is used to analyze the similarity of the voltage sequence of the points to be identified and the comprehensive similarity of all the faults of the target distribution transformer and the auxiliary distribution transformer, and set a similarity threshold to determine whether the points to be identified belong to the same station area. Finally, based on the probabilistic decision-making method, the identification of the topological relationship of the low-voltage station area household line change is completed. The experimental results show that this method can not only identify the topological relationship of single distribution transformer outage, but also identify the topological relationship of multiple distribution transformer outage. The accuracy of the identification result is high, and the identification loss function is low, which indicates that the identification result of this method is reliable and comprehensive.

Estimating 6D Aircraft Pose from Keypoints and Structures

This article addresses the challenge of 6D aircraft pose estimation from a single RGB image during the flight. Many recent works have shown that keypoints-based approaches, which first detect keypoints and then estimate the 6D pose, achieve remarkable performance. However, it is hard to locate the keypoints precisely in complex weather scenes. In this article, we propose a novel approach, called Pose Estimation with Keypoints and Structures (PEKS), which leverages multiple intermediate representations to estimate the 6D pose. Unlike previous works, our approach simultaneously locates keypoints and structures to recover the pose parameter of aircraft through a Perspective-n-Point Structure (PnPS) algorithm. These representations integrate the local geometric information of the object and the topological relationship between components of the target, which effectively improve the accuracy and robustness of 6D pose estimation. In addition, we contribute a dataset for aircraft pose estimation which consists of 3681 real images and 216,000 rendered images. Extensive experiments on our own aircraft pose dataset and multiple open-access pose datasets (e.g., ObjectNet3D, LineMOD) demonstrate that our proposed method can accurately estimate 6D aircraft pose in various complex weather scenes while achieving the comparative performance with the state-of-the-art pose estimation methods.

3D VIEWPOINT OPTIMIZATION OF TOPOLOGICAL RELATIONSHIPS: APPLICATION TO 3D CADASTRE FOR VISUAL EASEMENT VALIDATION

Offering optimum 3D viewpoint to user can be attractive in relieving occlusion in 3D scene. This could be much relevant for the visualization of 3D cadastral systems since they constitute complex datasets including both physical and legal objects while users are operating a number of visual tasks that require precise outlook. However, 3D viewpoint usability has yet to be evaluated to demonstrate its relevance in accomplishing given end user’s visual tasks. Hence, in this research project, the focus is set on visual identification of 3D topological relationships (disjoint and overlap) as it is one of the main users’ requirements in 3D cadastre. To this end, this paper addresses this issue using a virtual 3D model of the Planetarium Rio Tinto Alcan (Montreal city) in which property issues take place, especially regarding the easement validation procedure. Empirical tests have then been administrated in the form of interviews using an online questionnaire with university students who will specifically address such issues in their professional career. The results show that a 3D viewpoint that maximizes 3D disjoined or overlapped geometric objects’ view area within the viewport significantly outperforms traditional combined software points of view in visually identifying 3D topological relationship. This paper also suggests that user’s inexperience in 3D cadastre reduces visual task efficiency when visually identifying 3D topological relationship among overlapped geometric objects. Eventually, this study opens up new perspectives on 3D topological relationships modeling and visualization.