The multitude of airborne point clouds limits the point cloud processing efficiency.Superpoints are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve proces...The multitude of airborne point clouds limits the point cloud processing efficiency.Superpoints are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing efficiency.However,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a superpoint.Such feature inconsistencies degrade the performance of subsequent tasks.Thus,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented superpoints.Following the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is proposed.Experiments are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification errors.The experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.展开更多
In the field of remote sensing imaging, multispectral imaging can obtain an image of the observed scene in several bands, while the light detection and ranging(LiDAR) can acquire the accurate 3D geometric information ...In the field of remote sensing imaging, multispectral imaging can obtain an image of the observed scene in several bands, while the light detection and ranging(LiDAR) can acquire the accurate 3D geometric information of the scene. With the development of remote sensing technology, how to effectively integrate the two imaging technologies in order to collect and process simultaneous spectral and 3D geometric information has been one of the frontier problems. Most of the present researches on simultaneous spectral and geometric data acquisition focus on the design of physical multispectral LiDAR system, which inevitably lead to an imaging system of heavy weight and high power consumption and thus inconvenient in practice. Different from the present researches, a UAV-based integrated multispectral-LiDAR system is introduced in this paper. Through simultaneous multi-sensor data collection and multispectral point cloud generation, a low-cost and UAV-based portable 3D geometric and spectral information acquisition system can be achieved.展开更多
Multispectral points, as a new data source containing both spectrum and spatial geometry, opens the door to three-dimensional(3D) land cover classification at a finer scale. In this paper, we model the multispectral p...Multispectral points, as a new data source containing both spectrum and spatial geometry, opens the door to three-dimensional(3D) land cover classification at a finer scale. In this paper, we model the multispectral points as a graph and propose a multiattribute smooth graph convolutional network(Ma SGCN) for multispectral points classification. We construct the spatial graph,spectral graph, and geometric-spectral graph respectively to mine patterns in spectral, spatial, and geometric-spectral domains.Then, the multispectral points graph is generated by combining the spatial, spectral, and geometric-spectral graphs. Moreover,dimensionality features and spectrums are introduced to screen the appropriate connection points for constructing the spatial graph. For remote sensing scene classification tasks, it is usually desirable to make the classification map relatively smooth and avoid salt and pepper noise. A heat operator is then introduced to enhance the low-frequency filters and enforce the smoothness in the graph signal. Considering that different land covers have different scale characteristics, we use multiple scales instead of the single scale when leveraging heat operator on graph convolution. The experimental results on two real multispectral points data sets demonstrate the superiority of the proposed Ma SGCN to several state-of-the-art methods.展开更多
基金supported by the Youth Project of the National Natural Science Foundation of China(Grant No.62201237)the Yunnan Fundamental Research Projects(Grant Nos.202101BE070001-008 and202301AV070003)+1 种基金the Youth Project of the Xingdian Talent Support Plan of Yunnan Province(Grant No.KKRD202203068)the Major Science and Technology Projects in Yunnan Province(Grant No.202202AD080013)。
文摘The multitude of airborne point clouds limits the point cloud processing efficiency.Superpoints are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing efficiency.However,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a superpoint.Such feature inconsistencies degrade the performance of subsequent tasks.Thus,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented superpoints.Following the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is proposed.Experiments are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification errors.The experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.
基金supported by the National Natural Science Foundation of Key International Cooperation (Grant No. 61720106002)the Key Research and Development Project of Ministry of Science and Technology (Grant No.2017YFC1405100)the Heading Wild Goose Plan of Heilongjiang Province,China。
文摘In the field of remote sensing imaging, multispectral imaging can obtain an image of the observed scene in several bands, while the light detection and ranging(LiDAR) can acquire the accurate 3D geometric information of the scene. With the development of remote sensing technology, how to effectively integrate the two imaging technologies in order to collect and process simultaneous spectral and 3D geometric information has been one of the frontier problems. Most of the present researches on simultaneous spectral and geometric data acquisition focus on the design of physical multispectral LiDAR system, which inevitably lead to an imaging system of heavy weight and high power consumption and thus inconvenient in practice. Different from the present researches, a UAV-based integrated multispectral-LiDAR system is introduced in this paper. Through simultaneous multi-sensor data collection and multispectral point cloud generation, a low-cost and UAV-based portable 3D geometric and spectral information acquisition system can be achieved.
基金supported by the Key Research and Development Project of Ministry of Science and Technology(Grant No.2017YFC1405100)in part by the National Natural Science Foundation of Key International Cooperation(Grant No.61720106002)。
文摘Multispectral points, as a new data source containing both spectrum and spatial geometry, opens the door to three-dimensional(3D) land cover classification at a finer scale. In this paper, we model the multispectral points as a graph and propose a multiattribute smooth graph convolutional network(Ma SGCN) for multispectral points classification. We construct the spatial graph,spectral graph, and geometric-spectral graph respectively to mine patterns in spectral, spatial, and geometric-spectral domains.Then, the multispectral points graph is generated by combining the spatial, spectral, and geometric-spectral graphs. Moreover,dimensionality features and spectrums are introduced to screen the appropriate connection points for constructing the spatial graph. For remote sensing scene classification tasks, it is usually desirable to make the classification map relatively smooth and avoid salt and pepper noise. A heat operator is then introduced to enhance the low-frequency filters and enforce the smoothness in the graph signal. Considering that different land covers have different scale characteristics, we use multiple scales instead of the single scale when leveraging heat operator on graph convolution. The experimental results on two real multispectral points data sets demonstrate the superiority of the proposed Ma SGCN to several state-of-the-art methods.
