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Due to the limitation of less information in a single image, it is very difficult to generate a high-precision 3D model based on the image. There are some problems in the generation of 3D voxel models, e.g., the information loss at the upper level of a network. To solve these problems, we design a 3D model generation network based on multi-modal data constraints and multi-level feature fusion, named as 3DMGNet. Moreover, 3DMGNet is trained by self-supervised method to achieve 3D voxel model generation from an image. The image feature extraction network (2DNet) and 3D feature extraction network (3D auxiliary network) are used to extract the features of the image and 3D voxel model. Then, feature fusion is used to integrate the low-level features into the high-level features in the 3D auxiliary network. To extract more effective features, each layer of the feature map in feature extraction network is processed by an attention network. Finally, the extracted features generate 3D models by a 3D deconvolution network. The feature extraction of 3D model and the generation of voxelization play an auxiliary role in the training of the whole network for the 3D model generation based on an image. Additionally, a multi-view contour constraint method is proposed, to enhance the effect of the 3D model generation. In the experiment, the ShapeNet dataset is adapted to prove the effect of the 3DMGNet, which verifies the robust performance of the proposed method.
Ende Wang; Lei Xue; Yong Li; Zhenxin Zhang; Xukui Hou. 3DMGNet: 3D Model Generation Network Based on Multi-Modal Data Constraints and Multi-Level Feature Fusion. Sensors 2020, 20, 4875 .
AMA StyleEnde Wang, Lei Xue, Yong Li, Zhenxin Zhang, Xukui Hou. 3DMGNet: 3D Model Generation Network Based on Multi-Modal Data Constraints and Multi-Level Feature Fusion. Sensors. 2020; 20 (17):4875.
Chicago/Turabian StyleEnde Wang; Lei Xue; Yong Li; Zhenxin Zhang; Xukui Hou. 2020. "3DMGNet: 3D Model Generation Network Based on Multi-Modal Data Constraints and Multi-Level Feature Fusion." Sensors 20, no. 17: 4875.
The efficient separation of coal and gangue in the mining process is of great significance for improving coal mining efficiency and reducing environmental pollution. Automatic detection of coal and gangue is the key and foundation for the separation of coal and gangue. In this paper, we proposed a hierar-chical framework for coal and gangue detection based on deep learning models. In this framework, the Gauss-ian pyramid principle is first used to construct multi-level training data, leading to the sets of coal and gangue image features with multiple scales. Then, the coal and gangue regional proposal networks (CG-RPN) are designed to determine the candidate regions of the target objects in the image. Next, convolution neural net-works (CNNs) are constructed to recognize coal and gangue objects on the basis of extracted candidate re-gions. We performed our method on three different datasets. Experimental results showed that the proposed method improves the detection accuracy of coal and gangue objects by 0.8% compared with the previous methods, reaching up to 98.33%. In addition, our proposed method enables the detection of multiple coal and gangue objects in an individual image and solves the problem of queuing requirements in traditional methods.
Dongjun Li; Zhenxin Zhang; Zhihua Xu; Lili Xu; Guoying Meng; Zhen Li; Siyun Chen. An Image-Based Hierarchical Deep Learning Framework for Coal and Gangue Detection. IEEE Access 2019, 7, 184686 -184699.
AMA StyleDongjun Li, Zhenxin Zhang, Zhihua Xu, Lili Xu, Guoying Meng, Zhen Li, Siyun Chen. An Image-Based Hierarchical Deep Learning Framework for Coal and Gangue Detection. IEEE Access. 2019; 7 (99):184686-184699.
Chicago/Turabian StyleDongjun Li; Zhenxin Zhang; Zhihua Xu; Lili Xu; Guoying Meng; Zhen Li; Siyun Chen. 2019. "An Image-Based Hierarchical Deep Learning Framework for Coal and Gangue Detection." IEEE Access 7, no. 99: 184686-184699.
Accurate and effective classification of lidar point clouds with discriminative features expression is a challenging task for scene understanding. In order to improve the accuracy and the robustness of point cloud classification based on single point features, we propose a novel point set multi-level aggregation features extraction and fusion method based on multi-scale max pooling and latent Dirichlet allocation (LDA). To this end, in the hierarchical point set feature extraction, point sets of different levels and sizes are first adaptively generated through multi-level clustering. Then, more effective sparse representation is implemented by locality-constrained linear coding (LLC) based on single point features, which contributes to the extraction of discriminative individual point set features. Next, the local point set features are extracted by combining the max pooling method and the multi-scale pyramid structure constructed by the point’s coordinates within each point set. The global and the local features of the point sets are effectively expressed by the fusion of multi-scale max pooling features and global features constructed by the point set LLC-LDA model. The point clouds are classified by using the point set multi-level aggregation features. Our experiments on two scenes of airborne laser scanning (ALS) point clouds—a mobile laser scanning (MLS) scene point cloud and a terrestrial laser scanning (TLS) scene point cloud—demonstrate the effectiveness of the proposed point set multi-level aggregation features for point cloud classification, and the proposed method outperforms other related and compared algorithms.
Guofeng Tong; Yong Li; Weilong Zhang; Dong Chen; Jingchao Yang. Point Set Multi-Level Aggregation Feature Extraction Based on Multi-Scale Max Pooling and LDA for Point Cloud Classification. Remote Sensing 2019, 11, 2846 .
AMA StyleGuofeng Tong, Yong Li, Weilong Zhang, Dong Chen, Jingchao Yang. Point Set Multi-Level Aggregation Feature Extraction Based on Multi-Scale Max Pooling and LDA for Point Cloud Classification. Remote Sensing. 2019; 11 (23):2846.
Chicago/Turabian StyleGuofeng Tong; Yong Li; Weilong Zhang; Dong Chen; Jingchao Yang. 2019. "Point Set Multi-Level Aggregation Feature Extraction Based on Multi-Scale Max Pooling and LDA for Point Cloud Classification." Remote Sensing 11, no. 23: 2846.
Since the 1970s, land subsidence has been rapidly developing on the Beijing Plain, and the systematic study of the evolutionary mechanism of this subsidence is of great significance in the sustainable development of the regional economy. On the basis of Interferometric Synthetic Aperture Radar (InSAR) results, this study employed the Mann–Kendall method for the first time to determine the mutation information of land subsidence on the Beijing Plain from 2004 to 2015. By combining the hydrogeological conditions, “southern water” project, and other data, we attempted to analyse the reasons for land subsidence mutations. First, on the basis of ENVISAT ASAR and RADARSAT-2 data, the land subsidence of the Beijing Plain was determined while using small baseline interferometry (SBAS-InSAR) and Persistent Scatterers Interferometry (PSI). Second, on the basis of the Geographic Information System (GIS) platform, vector data of displacement under different scales were obtained. Through a series of tests, a scale of 960 metres was selected as the research unit and the displacement rate from 2004 to 2015 was obtained. Finally, a trend analysis of land subsidence was carried out on the basis of the Mann–Kendall mutation test. The results showed that single-year mutations were mainly distributed in the middle and lower parts of the Yongding River alluvial fan and the Chaobai River alluvial fan. Among these mutations, the greatest numbers occurred in 2015 and 2005, being 1344 and 915, respectively. The upper and middle alluvial fan of the Chaobai River, the vicinity of the emergency water sources, and the edge of the groundwater funnel have undergone several mutations. Combining hydrogeological data of the study area and the impact of the south-to-north water transfer project, we analysed the causes of these mutations. The experimental results can quantitatively verify the mutation information of land subsidence in conjunction with time series to further elucidate the spatial-temporal variation characteristics of land subsidence in the study area.
Lin Guo; Huili Gong; Feng Zhu; Lin Zhu; Zhenxin Zhang; Chaofan Zhou; Mingliang Gao; Yike Sun. Analysis of the Spatiotemporal Variation in Land Subsidence on the Beijing Plain, China. Remote Sensing 2019, 11, 1170 .
AMA StyleLin Guo, Huili Gong, Feng Zhu, Lin Zhu, Zhenxin Zhang, Chaofan Zhou, Mingliang Gao, Yike Sun. Analysis of the Spatiotemporal Variation in Land Subsidence on the Beijing Plain, China. Remote Sensing. 2019; 11 (10):1170.
Chicago/Turabian StyleLin Guo; Huili Gong; Feng Zhu; Lin Zhu; Zhenxin Zhang; Chaofan Zhou; Mingliang Gao; Yike Sun. 2019. "Analysis of the Spatiotemporal Variation in Land Subsidence on the Beijing Plain, China." Remote Sensing 11, no. 10: 1170.
Airborne laser scanning (ALS) point cloud classification is a challenge due to factors including complex scene structure, various densities, surface morphology, and the number of ground objects. A point cloud classification method is presented in this paper, based on content-sensitive multilevel objects (point clusters) in consideration of the density distribution of ground objects. The space projection method is first used to convert the three-dimensional point cloud into a two-dimensional (2D) image. The image is then mapped to the 2D manifold space, and restricted centroidal Voronoi tessellation is built for initial segmentation of content-sensitive point clusters. Thus, the segmentation results take the entity content (density distribution) into account, and the initial classification unit is adapted to the density of ground objects. The normalized cut is then used to segment the initial point clusters to construct content-sensitive multilevel point clusters. Following this, the point-based hierarchical features of each point cluster are extracted, and the multilevel point-cluster feature is constructed by sparse coding and latent Dirichlet allocation models. Finally, the hierarchical classification framework is created based on multilevel point-cluster features, and the AdaBoost classifiers in each level are trained. The recognition results of different levels are combined to effectively improve the classification accuracy of the ALS point cloud in the test process. Two scenes are used to experimentally test the method, and it is compared with three other state-of-the-art techniques.
Zongxia Xu; Zhenxin Zhang; Ruofei Zhong; Dong Chen; Taochun Sun; Xin Deng; Zhen Li; Cheng-Zhi Qin. Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification. Remote Sensing 2019, 11, 342 .
AMA StyleZongxia Xu, Zhenxin Zhang, Ruofei Zhong, Dong Chen, Taochun Sun, Xin Deng, Zhen Li, Cheng-Zhi Qin. Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification. Remote Sensing. 2019; 11 (3):342.
Chicago/Turabian StyleZongxia Xu; Zhenxin Zhang; Ruofei Zhong; Dong Chen; Taochun Sun; Xin Deng; Zhen Li; Cheng-Zhi Qin. 2019. "Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification." Remote Sensing 11, no. 3: 342.
This paper presents a novel framework to extract metro tunnel cross sections (profiles) from Terrestrial Laser Scanning point clouds. The entire framework consists of two steps: tunnel central axis extraction and cross section determination. In tunnel central extraction, we propose a slice-based method to obtain an initial central axis, which is further divided into linear and nonlinear circular segments by an enhanced Random Sample Consensus (RANSAC) tunnel axis segmentation algorithm. This algorithm transforms the problem of hybrid linear and nonlinear segment extraction into a sole segmentation of linear elements defined at the tangent space rather than raw data space, significantly simplifying the tunnel axis segmentation. The extracted axis segments are then provided as input to the step of the cross section determination which generates the coarse cross-sectional points by intersecting a series of straight lines that rotate orthogonally around the tunnel axis with their local fitted quadric surface, i.e., cylindrical surface. These generated profile points are further refined and densified via solving a constrained nonlinear least squares problem. Our experiments on Nanjing metro tunnel show that the cross sectional fitting error is only 1.69 mm. Compared with the designed radius of the metro tunnel, the RMSE (Root Mean Square Error) of extracted cross sections’ radii only keeps 1.60 mm. We also test our algorithm on another metro tunnel in Shanghai, and the results show that the RMSE of radii only keeps 4.60 mm which is superior to a state-of-the-art method of 6.00 mm. Apart from the accurate geometry, our approach can maintain the correct topology among cross sections, thereby guaranteeing the production of geometric tunnel model without crack defects. Moreover, we prove that our algorithm is insensitive to the missing data and point density.
Zhen Cao; Dong Chen; Yufeng Shi; Zhenxin Zhang; Fengxiang Jin; Ting Yun; Sheng Xu; Zhizhong Kang; Liqiang Zhang. A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds. Remote Sensing 2019, 11, 297 .
AMA StyleZhen Cao, Dong Chen, Yufeng Shi, Zhenxin Zhang, Fengxiang Jin, Ting Yun, Sheng Xu, Zhizhong Kang, Liqiang Zhang. A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds. Remote Sensing. 2019; 11 (3):297.
Chicago/Turabian StyleZhen Cao; Dong Chen; Yufeng Shi; Zhenxin Zhang; Fengxiang Jin; Ting Yun; Sheng Xu; Zhizhong Kang; Liqiang Zhang. 2019. "A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds." Remote Sensing 11, no. 3: 297.
Urban land cover and land use mapping plays an important role in urban planning and management. In this paper, novel multi-scale deep learning models, namely ASPP-Unet and ResASPP-Unet are proposed for urban land cover classification based on very high resolution (VHR) satellite imagery. The proposed ASPP-Unet model consists of a contracting path which extracts the high-level features, and an expansive path, which up-samples the features to create a high-resolution output. The atrous spatial pyramid pooling (ASPP) technique is utilized in the bottom layer in order to incorporate multi-scale deep features into a discriminative feature. The ResASPP-Unet model further improves the architecture by replacing each layer with residual unit. The models were trained and tested based on WorldView-2 (WV2) and WorldView-3 (WV3) imageries over the city of Beijing. Model parameters including layer depth and the number of initial feature maps (IFMs) as well as the input image bands were evaluated in terms of their impact on the model performances. It is shown that the ResASPP-Unet model with 11 layers and 64 IFMs based on 8-band WV2 imagery produced the highest classification accuracy (87.1% for WV2 imagery and 84.0% for WV3 imagery). The ASPP-Unet model with the same parameter setting produced slightly lower accuracy, with overall accuracy of 85.2% for WV2 imagery and 83.2% for WV3 imagery. Overall, the proposed models outperformed the state-of-the-art models, e.g., U-Net, convolutional neural network (CNN) and Support Vector Machine (SVM) model over both WV2 and WV3 images, and yielded robust and efficient urban land cover classification results.
Pengbin Zhang; Yinghai Ke; Zhenxin Zhang; Mingli Wang; Peng Li; Shuangyue Zhang. Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery. Sensors 2018, 18, 3717 .
AMA StylePengbin Zhang, Yinghai Ke, Zhenxin Zhang, Mingli Wang, Peng Li, Shuangyue Zhang. Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery. Sensors. 2018; 18 (11):3717.
Chicago/Turabian StylePengbin Zhang; Yinghai Ke; Zhenxin Zhang; Mingli Wang; Peng Li; Shuangyue Zhang. 2018. "Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery." Sensors 18, no. 11: 3717.
Line matching is the foundation of three-dimensional (3D) outline reconstruction for city buildings in aerial photogrammetry. Many existing studies have good line matching effects when dealing with aerial images with short baselines and small viewing angles. However, when faced with wide-baseline and large viewing-angle images, the matching effect drops sharply or even fails altogether. This paper deals with an efficient and simple method to achieve better line matching performance by a pair of wide-baseline aerial images, which make use of viewpoint-in variance to conduct line matching in rectified image spaces. Firstly, the perspective transformation relationship between the image plane and the geoid plane can be established from a Positioning and Orientation System (POS). Then, according to perspective projection matrices, two original images are separately rectified to conformal images, whose perspective deformation of large viewing-angle can be eliminated. Finally, the rectified images are used to conduct line matching, and the matched line segments obtained are back-projected to the original images. Four pairs of urban oblique aerial images are used to demonstrate the validity and efficiency of this method. Compared with line matching on original images, the number and the correctness of the matched line segments are greatly improved. Moreover, there is no loss of time efficiency. The proposed method can also be applied to general UAV (Unmanned Aerial Vehicle) aerial photogrammetry and introduced into matching for other geometric features, such as points, circles, curves, etc.
Qiang Wang; Haimeng Zhao; Zhenxin Zhang; Ximin Cui; Sana Ullah; Shanlin Sun; Fan Liu. Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images. Applied Sciences 2018, 8, 938 .
AMA StyleQiang Wang, Haimeng Zhao, Zhenxin Zhang, Ximin Cui, Sana Ullah, Shanlin Sun, Fan Liu. Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images. Applied Sciences. 2018; 8 (6):938.
Chicago/Turabian StyleQiang Wang; Haimeng Zhao; Zhenxin Zhang; Ximin Cui; Sana Ullah; Shanlin Sun; Fan Liu. 2018. "Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images." Applied Sciences 8, no. 6: 938.