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In December 2020, Chang’e-5 returned samples from mare basalt unit Em4/P58 in northern Oceanus Procellarum. With an absolute model age of 1.21-1.53 Ga (Hiesinger et al. 2003; Qian et al., 2018, 2021a). Elevation analysis of the interior of Rima Sharp and Mairan, plus geomorphological observations suggest lava backfilling of Rima Sharp with younger lava from Rima Mairan. Analysis of the magnetic field at the surface also shows that the younger lava from the southern Mairan vents, which covered the southeastern part of Em4/P58 is unmagnetized. Our new magnetic anomaly analysis shows that Em4/P58 unit can be divided into magnetized (northwest) and unmagnetized (southeast) parts. The comprehensive analysis of magnetic anomaly and topography shows that lava from southern rille is likely to have reached the Chang’e-5 landing site. Samples returned by Chang’e-5 will help constrain the source of lava flow and the evolution of the lunar magnetic field over time.
Teng Hu; Xiaojian XuiD; Shuo Yao; Zhizhong KangiD; Xiaoyun WaniD; Carolyn H. van der Bogert; Harald Hiesinger. Magnetic Signature of Basalts in the Chang'e-5 Sample Region: Implications for the Lunar Dynamo. 2021, 1 .
AMA StyleTeng Hu, Xiaojian XuiD, Shuo Yao, Zhizhong KangiD, Xiaoyun WaniD, Carolyn H. van der Bogert, Harald Hiesinger. Magnetic Signature of Basalts in the Chang'e-5 Sample Region: Implications for the Lunar Dynamo. . 2021; ():1.
Chicago/Turabian StyleTeng Hu; Xiaojian XuiD; Shuo Yao; Zhizhong KangiD; Xiaoyun WaniD; Carolyn H. van der Bogert; Harald Hiesinger. 2021. "Magnetic Signature of Basalts in the Chang'e-5 Sample Region: Implications for the Lunar Dynamo." , no. : 1.
The increasing availability of both indoor positioning services and sensors for 3D data capture, such as RGB-D sensors, allows the provision of indoor spatial information services for indoor localization-based applications. To efficiently realize these services, the indoor information and the relationships between indoor spaces are required. The recently released Indoor Geography Markup Language (IndoorGML) attempts to represent and exchange geo-information for modeling topology and semantics of indoor spaces. However, it is still challenging to map indoor space features to the IndoorGML-encoded navigation network model directly from colorized 3D points. Therefore, we propose a semantics-guided method for indoor navigation element reconstruction from RGB-D sensor data. First, a hierarchical indoor scene interpretation framework is used for robustly recognizing the architecture structures and doors, respectively. In the developed hierarchical structure, a graph convolutional network-based architectural structure recognition method is adopted to deduce the long-range interactions among primitives for describing the rich physical relationships in the real world. Its output is the produced initial annotated results, from which doors as the common openings are further detected using a U-Net-based door recognition method. This enables to effectively provide the semantic guidance for the cellular representation of the indoor space and its topological reconstruction. Second, an adaptive architectural structure-guided room segmentation method is developed by combining distance transform and watershed segmentation to determine cellular spaces according to the definition in IndoorGML. Third, taking the different states of doors into consideration, a door-guided topological relationship reconstruction method is proposed to achieve the network graph representation of indoor environments. In this context, a simulated door model is designed to correct and update the true position of a door leaf, and a virtual door is defined to optimize the topological analysis. As a consequence, an IndoorGML-encoded navigation network model is generated, which can be used as the base for indoor navigation applications independent of the platform. Experiments are performed on the public Stanford large-scale 3D Indoor Spaces Dataset to verify the robustness and effectiveness of the proposed method both qualitatively and quantitatively. Results indicate the capability of the proposed method in automatically reconstructing indoor navigation elements of Manhattan-world indoor environments from RGB-D sensor data.
Juntao Yang; Zhizhong Kang; Liping Zeng; Perpetual Hope Akwensi; Monika Sester. Semantics-guided reconstruction of indoor navigation elements from 3D colorized points. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 173, 238 -261.
AMA StyleJuntao Yang, Zhizhong Kang, Liping Zeng, Perpetual Hope Akwensi, Monika Sester. Semantics-guided reconstruction of indoor navigation elements from 3D colorized points. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 173 ():238-261.
Chicago/Turabian StyleJuntao Yang; Zhizhong Kang; Liping Zeng; Perpetual Hope Akwensi; Monika Sester. 2021. "Semantics-guided reconstruction of indoor navigation elements from 3D colorized points." ISPRS Journal of Photogrammetry and Remote Sensing 173, no. : 238-261.
As a semi-enclosed crater basin on the northwest rim of Imbrium basin, Sinus Iridum is a key site to investigate the geological characteristics at the intersection of two basins. For this reason, we focused on model age determination in Sinus Iridum basin using Chang’E−2 high-resolution images coupled with compositional maps from the Clementine data sets, as well as with digital elevation models (DEMs) from the Lunar Orbiter Laser Altimeter (LOLA) onboard the Lunar Reconnaissance Orbiter (LRO). With these datasets we identified different geologic units onto which we performed model age determinations based on crater counting. This systematic analysis of the Sinus Iridum basin shows that the age of the oldest exposed basaltic unit is 3.37 Ga (Imbrian age), while the youngest, a mare basalt unit that enters the basin from Imbrium basin, is 1.24 Ga (late Eratosthenian). In general, the ages of the geologic units inside the Sinus Iridum basin gradually decrease from the northeast to the southwest, with the only exception of the young units being located in the north-eastern area. We conclude that the crater size-frequency distributions (CSFD) reflect a multi-layer sequence, suggesting multiple resurfacing events inside Sinus Iridum. The model age determinations identify several infilling events of basalts ranging from 3.37 Ga to 1.24 Ga, which are all derived mare basalt flows from the Imbrium basin.
Teng Hu; Zhizhong Kang; Matteo Massironi; Harald Hiesinger; Carolyn H. van der Bogert; Paolo Gamba; Maria Teresa Brunetti; Maria Teresa Melis. Geological evolution of the Sinus Iridum basin. Planetary and Space Science 2020, 194, 105134 .
AMA StyleTeng Hu, Zhizhong Kang, Matteo Massironi, Harald Hiesinger, Carolyn H. van der Bogert, Paolo Gamba, Maria Teresa Brunetti, Maria Teresa Melis. Geological evolution of the Sinus Iridum basin. Planetary and Space Science. 2020; 194 ():105134.
Chicago/Turabian StyleTeng Hu; Zhizhong Kang; Matteo Massironi; Harald Hiesinger; Carolyn H. van der Bogert; Paolo Gamba; Maria Teresa Brunetti; Maria Teresa Melis. 2020. "Geological evolution of the Sinus Iridum basin." Planetary and Space Science 194, no. : 105134.
The Rümker region is located in the northern Oceanus Procellarum, which has been selected as the landing and sampling region for China’s Chang’e-5 (CE-5) mission. The thermophysical features of the mare units are studied in detail using the brightness temperature (TB) maps (TB, normalized TB, TB difference) derived from the CE-2 microwave radiometer data. The previously interpreted geological boundaries of the Rümker region are revisited in this study according to their TB behaviors: IR1, IR2, and IR3 Rümker plateau units are combined into one single unit (IR); and a hidden unit is found on the Mons Rümker; Mare basaltic units Im1 and Em1 are combined into Em1; and Em2 is more likely the extending of Im2. Each of the previous proposed landing sites and their scientific value are summarized and reevaluated. Based on this, four landing sites are recommended in order to maximize the scientific outcome of the CE-5 mission. We suggest that the Eratosthenian-aged Em4 and Em1 units as the top priority landing site for the CE-5 mission; the age-dating results will provide important clues concerning the thermal evolution of the Moon.
Zhiguo Meng; Jietao Lei; Yuqi Qian; Long Xiao; James Head; Shengbo Chen; Weiming Cheng; Jiancheng Shi; Jinsong Ping; Zhizhong Kang. Thermophysical Features of the Rümker Region in Northern Oceanus Procellarum: Insights from CE-2 CELMS Data. Remote Sensing 2020, 12, 3272 .
AMA StyleZhiguo Meng, Jietao Lei, Yuqi Qian, Long Xiao, James Head, Shengbo Chen, Weiming Cheng, Jiancheng Shi, Jinsong Ping, Zhizhong Kang. Thermophysical Features of the Rümker Region in Northern Oceanus Procellarum: Insights from CE-2 CELMS Data. Remote Sensing. 2020; 12 (19):3272.
Chicago/Turabian StyleZhiguo Meng; Jietao Lei; Yuqi Qian; Long Xiao; James Head; Shengbo Chen; Weiming Cheng; Jiancheng Shi; Jinsong Ping; Zhizhong Kang. 2020. "Thermophysical Features of the Rümker Region in Northern Oceanus Procellarum: Insights from CE-2 CELMS Data." Remote Sensing 12, no. 19: 3272.
Airborne light detection and ranging (LiDAR) point clouds have become growingly popular as a reliable data source for 3-D digital building model reconstruction. Therefore, we develop a label-constraint approach for automatically detecting building roofs using airborne LiDAR point clouds and multispectral images, where the label information is introduced in both the discriminative feature space generation and the detection procedure. To obtain a robust and highly discriminative descriptor, a supervised sparse coding-enhanced bag of visual word (SC-BOVW) model based on a learned discriminative dictionary is used to encode local geometric and spectral information within each super-voxel into high-level semantic representation, which is then fed into a support vector machine (SVM) classifier for distinguishing buildings from others. Additionally, a graph cut-based procedure is used as a postprocessing step to guarantee the spatial consistency in detection results. Experiments were conducted on the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark data sets. Results indicate that the proposed method is accurate and efficient in terms of building roof region detection. Moreover, the proposed method is superior to other existing methods with average differences in recall of 2.23%, precision of 0.28% and quality of 1.99%.
Juntao Yang; Zhizhong Kang; Perpetual Hope Akwensi. A Label-Constraint Building Roof Detection Method From Airborne LiDAR Point Clouds. IEEE Geoscience and Remote Sensing Letters 2020, 18, 1466 -1470.
AMA StyleJuntao Yang, Zhizhong Kang, Perpetual Hope Akwensi. A Label-Constraint Building Roof Detection Method From Airborne LiDAR Point Clouds. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (8):1466-1470.
Chicago/Turabian StyleJuntao Yang; Zhizhong Kang; Perpetual Hope Akwensi. 2020. "A Label-Constraint Building Roof Detection Method From Airborne LiDAR Point Clouds." IEEE Geoscience and Remote Sensing Letters 18, no. 8: 1466-1470.
Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to indoor environment reconstruction. Therefore, this paper reviews the state-of-the-art techniques for the three-dimensional (3D) reconstruction of indoor environments. First, some of the available benchmark datasets for 3D reconstruction of indoor environments are described and discussed. Then, data collection of 3D indoor spaces is briefly summarized. Furthermore, an overview of the geometric, semantic, and topological reconstruction of the indoor environment is presented, where the existing methodologies, advantages, and disadvantages of these three reconstruction types are analyzed and summarized. Finally, future research directions, including technique challenges and trends, are discussed for the purpose of promoting future research interest. It can be concluded that most of the existing indoor environment reconstruction methods are based on the strong Manhattan assumption, which may not be true in a real indoor environment, hence limiting the effectiveness and robustness of existing indoor environment reconstruction methods. Moreover, based on the hierarchical pyramid structures and the learnable parameters of deep-learning architectures, multi-task collaborative schemes to share parameters and to jointly optimize each other using redundant and complementary information from different perspectives show their potential for the 3D reconstruction of indoor environments. Furthermore, indoor–outdoor space seamless integration to achieve a full representation of both interior and exterior buildings is also heavily in demand.
Zhizhong Kang; Juntao Yang; Zhou Yang; Sai Cheng. A Review of Techniques for 3D Reconstruction of Indoor Environments. ISPRS International Journal of Geo-Information 2020, 9, 330 .
AMA StyleZhizhong Kang, Juntao Yang, Zhou Yang, Sai Cheng. A Review of Techniques for 3D Reconstruction of Indoor Environments. ISPRS International Journal of Geo-Information. 2020; 9 (5):330.
Chicago/Turabian StyleZhizhong Kang; Juntao Yang; Zhou Yang; Sai Cheng. 2020. "A Review of Techniques for 3D Reconstruction of Indoor Environments." ISPRS International Journal of Geo-Information 9, no. 5: 330.
Global geo-reference Lunar Reconnaissance Orbiter Camera-wide angle camera (LROC-WAC) mosaic imagery provides the precise geographic information for the mapping of mineral elements based on Chang'E-1 interferometric imaging spectrometer (IIM) imagery. However, the traditional image registration methods fail to achieve the accurate registration in between due to heterogeneous characteristics. Therefore, this letter proposes a semiautomatic registration method for Chang'E-1 IIM imagery based on global geo-reference LROC-WAC mosaic imagery. Due to the lack of ground control points, the method implemented a random sample consensus (RANSAC)-guided affine transformation (AT) model to help predict the potential coarse correspondence. Afterward, a multiwindow image matching approach is performed for the fine correspondence. To verify the performance of the proposed method, experiments were performed using Chang'E-1 IIM imagery and geo-reference LROC-WAC mosaic imagery. Experimental results indicate that the proposed method can obtain a massive number of homologous points while minimizing manual intervention, which is comparable to manual results and has high image registration accuracy.
Ze Yang; Zhizhong Kang; Juntao Yang. A Semiautomatic Registration Method for Chang’E-1 IIM Imagery Based on Globally Geo-Reference LROC-WAC Mosaic Imagery. IEEE Geoscience and Remote Sensing Letters 2020, 18, 543 -547.
AMA StyleZe Yang, Zhizhong Kang, Juntao Yang. A Semiautomatic Registration Method for Chang’E-1 IIM Imagery Based on Globally Geo-Reference LROC-WAC Mosaic Imagery. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (3):543-547.
Chicago/Turabian StyleZe Yang; Zhizhong Kang; Juntao Yang. 2020. "A Semiautomatic Registration Method for Chang’E-1 IIM Imagery Based on Globally Geo-Reference LROC-WAC Mosaic Imagery." IEEE Geoscience and Remote Sensing Letters 18, no. 3: 543-547.
Accurate individual tree segmentation is an important basis for the subsequent calculation and analysis of forestry parameters. However, rasterized Canopy Height Model (CHM)-based methods often suffer from 3D information loss due to the interpolation operation. Therefore, this paper proposes an individual tree segmentation method based on the marker-controlled watershed algorithm and 3D spatial distribution analysis from airborne LiDAR point clouds. First, based on the potential tree apices derived from the local maxima filtering, the marker-controlled watershed segmentation algorithm is conducted to obtain the coarse point clusters. Then, within the Principal Component Analysis (PCA)-defined local coordinate reference framework, a multi-directional 3D spatial profile analysis is performed on each point cluster to refine the potential tree apex positions. Finally, the refined potential tree apex positions are used as a prior of K-means clustering to achieve the coarse-to-fine individual tree segmentation. Comparative experiments were conducted on the public NEWFOR dataset to evaluate the proposed method. Results indicate that the proposed method is efficient and robust for segmenting individual trees.
Juntao Yang; Zhizhong Kang; Sai Cheng; Zhou Yang; Perpetual Hope Akwensi. An Individual Tree Segmentation Method Based on Watershed Algorithm and Three-Dimensional Spatial Distribution Analysis From Airborne LiDAR Point Clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 1055 -1067.
AMA StyleJuntao Yang, Zhizhong Kang, Sai Cheng, Zhou Yang, Perpetual Hope Akwensi. An Individual Tree Segmentation Method Based on Watershed Algorithm and Three-Dimensional Spatial Distribution Analysis From Airborne LiDAR Point Clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):1055-1067.
Chicago/Turabian StyleJuntao Yang; Zhizhong Kang; Sai Cheng; Zhou Yang; Perpetual Hope Akwensi. 2020. "An Individual Tree Segmentation Method Based on Watershed Algorithm and Three-Dimensional Spatial Distribution Analysis From Airborne LiDAR Point Clouds." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 1055-1067.
Hui Wang; Teng Hu; Zhen Wang; Zhizhong Kang; Perpetual Hope Akwensi; Juntao Yang. Reconstruction of Power Pylons From LiDAR Point Clouds Based on Structural Segmentation and Parameter Estimation. IEEE Geoscience and Remote Sensing Letters 2020, 1 -5.
AMA StyleHui Wang, Teng Hu, Zhen Wang, Zhizhong Kang, Perpetual Hope Akwensi, Juntao Yang. Reconstruction of Power Pylons From LiDAR Point Clouds Based on Structural Segmentation and Parameter Estimation. IEEE Geoscience and Remote Sensing Letters. 2020; ():1-5.
Chicago/Turabian StyleHui Wang; Teng Hu; Zhen Wang; Zhizhong Kang; Perpetual Hope Akwensi; Juntao Yang. 2020. "Reconstruction of Power Pylons From LiDAR Point Clouds Based on Structural Segmentation and Parameter Estimation." IEEE Geoscience and Remote Sensing Letters , no. : 1-5.
Point cloud feature extraction as a classification task is crucial in maximizing the efficient downstream applicability of raw point clouds. With the goal of learning optimum features for efficient classification of a multi-class point cloud for downstream applications, this letter presents a supervoxel-Fisher vector (FV)-based approach for airborne light detection and ranging (LiDAR) data classification. In our approach, FV encoding is implemented to deduce compact global descriptors from aggregated supervoxels to establish a more descriptive and discriminative representation, transforming the low-level visual features into high-level semantic features. As a result, the proposed approach combines local and global feature properties through the quantization and aggregation of higher order statistics to harnesses their combined advantages for producing good classification results. Experiments were conducted on the international society for photogrammetry and remote sensing 3-D semantic labeling benchmark data set. Results indicate that the proposed approach is robust and efficient, attained the third best position with an overall accuracy of 81.79%, and ranked first with an F₁-score of 72.31%.
Perpetual Hope Akwensi; Zhizhong Kang; Juntao Yang. Fisher Vector Encoding of Supervoxel-Based Features for Airborne LiDAR Data Classification. IEEE Geoscience and Remote Sensing Letters 2019, 17, 504 -508.
AMA StylePerpetual Hope Akwensi, Zhizhong Kang, Juntao Yang. Fisher Vector Encoding of Supervoxel-Based Features for Airborne LiDAR Data Classification. IEEE Geoscience and Remote Sensing Letters. 2019; 17 (3):504-508.
Chicago/Turabian StylePerpetual Hope Akwensi; Zhizhong Kang; Juntao Yang. 2019. "Fisher Vector Encoding of Supervoxel-Based Features for Airborne LiDAR Data Classification." IEEE Geoscience and Remote Sensing Letters 17, no. 3: 504-508.
Impact craters are among the most noticeable geomorphological features on the planetary surface and yield significant information about terrain evolution and the history of the solar system. Thus, the recognition of impact craters is an important branch of modern planetary studies. Aiming at addressing problems associated with the insufficient and inaccurate detection of lunar impact craters, a decision fusion method within the Bayesian network (BN) framework is developed in this paper to handle multi-source information from both optical images and associated digital elevation model (DEM) data. First, we implement the edge-based method for efficiently searching crater candidates which are the image patches that can potentially contain impact craters. Secondly, the multi-source representations of an impact crater derived from both optical images and DEM data are proposed and constructed to quantitatively describe the two-dimensional (2D) and three-dimensional (3D) morphology, consisting of Histogram of Oriented Gradient (HOG), Histogram of Multi-scale Slope (HMS) and Histogram of Multi-scale Aspect (HMA). Finally, a BN-based framework integrates the multi-source representations of impact craters, which can provide reductant and complementary information, for distinguishing craters from non-craters. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on three lunar scenes using both orthoimages from the Lunar Reconnaissance Orbiter (LRO) and DEM data acquired by the Lunar Orbiter Laser Altimeter (LOLA). Experimental results demonstrate that integrating optical images with DEM data significantly decreases the number of false positives compared with using optical images alone, with F1-score of 84.8% on average. Moreover, compared with other existing fusion methods, our proposed method was quite advantageous especially for the detection of small-scale craters with diameters less than 1000m.
Juntao Yang; Zhizhong. Kang. Bayesian network-based extraction of lunar impact craters from optical images and DEM data. Advances in Space Research 2019, 63, 3721 -3737.
AMA StyleJuntao Yang, Zhizhong. Kang. Bayesian network-based extraction of lunar impact craters from optical images and DEM data. Advances in Space Research. 2019; 63 (11):3721-3737.
Chicago/Turabian StyleJuntao Yang; Zhizhong. Kang. 2019. "Bayesian network-based extraction of lunar impact craters from optical images and DEM data." Advances in Space Research 63, no. 11: 3721-3737.
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.
Juntao Yang; Zhizhong Kang; Perpetual Hope Akwensi. A Skeleton-Based Hierarchical Method for Detecting 3-D Pole-Like Objects From Mobile LiDAR Point Clouds. IEEE Geoscience and Remote Sensing Letters 2018, 16, 801 -805.
AMA StyleJuntao Yang, Zhizhong Kang, Perpetual Hope Akwensi. A Skeleton-Based Hierarchical Method for Detecting 3-D Pole-Like Objects From Mobile LiDAR Point Clouds. IEEE Geoscience and Remote Sensing Letters. 2018; 16 (5):801-805.
Chicago/Turabian StyleJuntao Yang; Zhizhong Kang; Perpetual Hope Akwensi. 2018. "A Skeleton-Based Hierarchical Method for Detecting 3-D Pole-Like Objects From Mobile LiDAR Point Clouds." IEEE Geoscience and Remote Sensing Letters 16, no. 5: 801-805.
The velocity of an ice shelf is an important characteristic to understand its dynamics and interaction with the internal ice sheet. Therefore, we develop an improved multi-scale image matching method for producing a complete and accurate Amery ice shelf velocity field from Landsat 8 images. First, we investigate the relationship between the template size and the image entropy and propose an image entropy-based preliminary operation for distinguishing the high-contrast regions from the low-contrast regions prior to iteratively determining the optimum template size. Second, a Gaussian pyramid image-based hierarchical data structure is designed to support a coarse-to-fine image matching strategy.The image entropy-based matching method is applied on the top layer of the image pyramid to guarantee the matching results have optimal completeness and high accuracy. Finally, a postprocess procedure is performed to derive a complete and accurate Amery ice shelf velocity field. Experimental results demonstrate that the proposed method can significantly improve computational efficiency. Moreover, the proposed method provides more accurate and robust matching results than other existing methods, particularly over the low-contrast surfaces. Additionally, the derived velocity field also shows good consistency with the one acquired from MEaSUREs Annual Antarctic Ice Velocity Maps 2016–2017.
Ze Yang; Zhizhong Kang; Xiao Cheng; Juntao Yang. Improved multi-scale image matching approach for monitoring Amery ice shelf velocity using Landsat 8. European Journal of Remote Sensing 2018, 52, 56 -72.
AMA StyleZe Yang, Zhizhong Kang, Xiao Cheng, Juntao Yang. Improved multi-scale image matching approach for monitoring Amery ice shelf velocity using Landsat 8. European Journal of Remote Sensing. 2018; 52 (1):56-72.
Chicago/Turabian StyleZe Yang; Zhizhong Kang; Xiao Cheng; Juntao Yang. 2018. "Improved multi-scale image matching approach for monitoring Amery ice shelf velocity using Landsat 8." European Journal of Remote Sensing 52, no. 1: 56-72.
Mobile Laser Scanning (MLS) point cloud data contains rich three-dimensional (3D) information on road ancillary facilities such as street lamps, traffic signs and utility poles. Automatically recognizing such information from point cloud would provide benefits for road safety inspection, ancillary facilities management and so on, and can also provide basic information support for the construction of an information city. This paper presents a method for extracting and classifying pole-like objects (PLOs) from unstructured MLS point cloud data. Firstly, point cloud is preprocessed to remove outliers, downsample and filter ground points. Then, the PLOs are extracted from the point cloud by spatial independence analysis and cylindrical or linear feature detection. Finally, the PLOs are automatically classified by 3D shape matching. The method was tested based on two point clouds with different road environments. The completeness, correctness and overall accuracy were 92.7%, 97.4% and 92.3% respectively in Data I. For Data II, that provided by International Society for Photogrammetry and Remote Sensing Working Group (ISPRS WG) III/5 was also used to test the performance of the method, and the completeness, correctness and overall accuracy were 90.5%, 97.1% and 91.3%, respectively. Experimental results illustrate that the proposed method can effectively extract and classify PLOs accurately and effectively, which also shows great potential for further applications of MLS point cloud data.
Zhenwei Shi; Zhizhong Kang; Yi Lin; Yu Liu; Wei Chen. Automatic Recognition of Pole-Like Objects from Mobile Laser Scanning Point Clouds. Remote Sensing 2018, 10, 1891 .
AMA StyleZhenwei Shi, Zhizhong Kang, Yi Lin, Yu Liu, Wei Chen. Automatic Recognition of Pole-Like Objects from Mobile Laser Scanning Point Clouds. Remote Sensing. 2018; 10 (12):1891.
Chicago/Turabian StyleZhenwei Shi; Zhizhong Kang; Yi Lin; Yu Liu; Wei Chen. 2018. "Automatic Recognition of Pole-Like Objects from Mobile Laser Scanning Point Clouds." Remote Sensing 10, no. 12: 1891.
Lunar impact craters form the basis for lunar geological stratigraphy, and small-scale craters further enrich the basic statistical data for the estimation of local geological ages. Thus, the extraction of lunar impact craters is an important branch of modern planetary studies. However, few studies have reported on the extraction of small-scale craters. Therefore, this paper proposes a coarse-to-fine resolution method to automatically extract small-scale impact craters from charge-coupled device (CCD) images using histogram of oriented gradient (HOG) features and a support vector machine (SVM) classifier. First, large-scale craters are extracted as samples from the Chang'E-1 images with spatial resolutions of 120 m. The SVM classifier is then employed to establish the criteria for classifying craters and noncraters from the HOG features of the extracted samples. The criteria are then used to extract small-scale craters from higher resolution Chang'E-2 CCD images with spatial resolutions of 1.4, 7, and 50 m. The sample database is updated with the newly extracted small-scale craters for the purpose of the progressive optimization of the extraction. The proposed method is tested on both simulated images and multiple resolutions of real CCD images acquired by the Chang'E orbiters and provides high accuracy results in the extraction of the small-scale impact craters, the smallest of which is 20 m.
Zhizhong Kang; Xingkun Wang; Teng Hu; Juntao Yang. Coarse-to-Fine Extraction of Small-Scale Lunar Impact Craters From the CCD Images of the Chang’E Lunar Orbiters. IEEE Transactions on Geoscience and Remote Sensing 2018, 57, 181 -193.
AMA StyleZhizhong Kang, Xingkun Wang, Teng Hu, Juntao Yang. Coarse-to-Fine Extraction of Small-Scale Lunar Impact Craters From the CCD Images of the Chang’E Lunar Orbiters. IEEE Transactions on Geoscience and Remote Sensing. 2018; 57 (1):181-193.
Chicago/Turabian StyleZhizhong Kang; Xingkun Wang; Teng Hu; Juntao Yang. 2018. "Coarse-to-Fine Extraction of Small-Scale Lunar Impact Craters From the CCD Images of the Chang’E Lunar Orbiters." IEEE Transactions on Geoscience and Remote Sensing 57, no. 1: 181-193.
The digital mapping of road environment is an important task for road infrastructure inventory and urban planning. Automatic extraction and classification of pole-like objects can remarkably reduce mapping cost and enhance work efficiency. Therefore, this paper proposes a voxel-based method that automatically extracts and classifies three-dimensional (3-D) pole-like objects by analyzing the spatial characteristics of objects. First, a voxel-based shape recognition is conducted to generate a set of pole-like object candidates. Second, according to their isolation and vertical continuity, the pole-like objects are detected and individualized using the proposed circular model with an adaptive radius and the vertical region growing algorithm. Finally, several semantic rules, consisting of shape features and spatial topological relationships, are derived for further classifying the extracted pole-like objects into four categories (i.e., lamp posts, utility poles, tree trunks, and others). The proposed method was evaluated using three datasets from mobile LiDAR point cloud data. The experimental results demonstrate that the proposed method efficiently extracted the pole-like objects from the three datasets, with extraction rates of 85.3%, 94.1%, and 92.3%. Moreover, the proposed method can achieve robust classification results, especially for classifying tree trunks.
Zhizhong Kang; Juntao Yang; Ruofei Zhong; Yongxing Wu; Zhenwei Shi; Roderik Lindenbergh. Voxel-Based Extraction and Classification of 3-D Pole-Like Objects From Mobile LiDAR Point Cloud Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2018, 11, 4287 -4298.
AMA StyleZhizhong Kang, Juntao Yang, Ruofei Zhong, Yongxing Wu, Zhenwei Shi, Roderik Lindenbergh. Voxel-Based Extraction and Classification of 3-D Pole-Like Objects From Mobile LiDAR Point Cloud Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018; 11 (11):4287-4298.
Chicago/Turabian StyleZhizhong Kang; Juntao Yang; Ruofei Zhong; Yongxing Wu; Zhenwei Shi; Roderik Lindenbergh. 2018. "Voxel-Based Extraction and Classification of 3-D Pole-Like Objects From Mobile LiDAR Point Cloud Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 11: 4287-4298.
The safety of the electricity infrastructure significantly affects both our daily life and industrial activities. Timely and accurate monitoring of the safety of electricity network can prevent dangerous situations effectively. Thus, we, in this paper, develop a voxel-based method for automatically extracting the transmission lines from airborne LiDAR point cloud data. The method proposed in this paper uses three-dimensional (3-D) voxels as primitives and consist of the following steps: First, skeleton structure extraction using Laplacian smoothing; second, feature construction of a 3-D voxel using Latent Dirichlet allocation topic model; and third Markov random field model-based extraction for generating locally continuous and globally optimal results. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on four different types of power line scenes with flat and complex terrains from helicopter-borne LiDAR point cloud data. Experimental results demonstrate that our proposed method is efficient and robust for automatically detecting both the single conductor and the bundled conductors, with precision, recall, and quality of over 96.78%, 98.67%, and 96.66%, respectively. Moreover, compared with other existing methods, our proposed method provides higher detection correctness rate.
Juntao Yang; Zhizhong Kang. Voxel-Based Extraction of Transmission Lines From Airborne LiDAR Point Cloud Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2018, 11, 3892 -3904.
AMA StyleJuntao Yang, Zhizhong Kang. Voxel-Based Extraction of Transmission Lines From Airborne LiDAR Point Cloud Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018; 11 (10):3892-3904.
Chicago/Turabian StyleJuntao Yang; Zhizhong Kang. 2018. "Voxel-Based Extraction of Transmission Lines From Airborne LiDAR Point Cloud Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 10: 3892-3904.
Mobile Light Detection And Ranging (LiDAR) point clouds have the characteristics of complex and incomplete scenes, uneven point density and noises, which raises great challenges for automatically interpreting 3D scene. Aiming at the problem of 3D point cloud classification, we propose a probabilistic graphical model for automatic classification of mobile LiDAR point clouds in this paper. First, the super-voxels are generated as primitives based on the similar geometric and radiometric properties. Second, we construct point-based multi-scale visual features that are used to describe the texture information at various scales. Third, the topic model is used to analyze the semantic correlations among points within super-voxels to establish the semantic representation, which is finally fed into the proposed probabilistic graphical model. The proposed model combines Bayesian network and Markov random fields to obtain locally continuous and globally optimal classification results. To evaluate the effectiveness and the robustness of the proposed method, experiments were conducted using mobile LiDAR point clouds for three types of street scenes. Experimental results demonstrate that our proposed model is efficient and robust for extracting vehicles, buildings, street trees and pole-like objects, with overall accuracies of 98.17%, 97.41% and 96.81% respectively. Moreover, compared with other existing methods, our proposed model can provide higher classification correctness, specifically for small objects such as cars and pole-like objects.
Zhizhong Kang; Juntao Yang. A probabilistic graphical model for the classification of mobile LiDAR point clouds. ISPRS Journal of Photogrammetry and Remote Sensing 2018, 143, 108 -123.
AMA StyleZhizhong Kang, Juntao Yang. A probabilistic graphical model for the classification of mobile LiDAR point clouds. ISPRS Journal of Photogrammetry and Remote Sensing. 2018; 143 ():108-123.
Chicago/Turabian StyleZhizhong Kang; Juntao Yang. 2018. "A probabilistic graphical model for the classification of mobile LiDAR point clouds." ISPRS Journal of Photogrammetry and Remote Sensing 143, no. : 108-123.
Landslides have been observed on several planets and minor bodies of the solar System, including the Moon. Notwithstanding different types of slope failures have been studied on the Moon, a detailed lunar landslide inventory is still pending. Undoubtedly, such will be in a benefit for future geological and morphological studies, as well in hazard, risk and susceptibility assessments. A preliminary survey of lunar landslides in impact craters has been done using visual inspection on images and digital elevation model (DEM) (Brunetti et al. 2015) but this method suffers from subjective interpretation. A new methodology based on polynomial interpolation of crater cross-sections extracted from global lunar DEMs is presented in this paper. Because of their properties, Chebyshev polynomials were already exploited for parametric classification of different crater morphologies (Mahanti et al., 2014). Here, their use has been extended to the discrimination of slumps in simple impact craters. Two criteria for recognition have provided the best results: one based on fixing an empirical absolute thresholding and a second based on statistical adaptive thresholding. The application of both criteria to a data set made up of 204 lunar craters’ cross-sections has demonstrated that the former criterion provides the best recognition.
Marco Scaioni; Vasil Yordanov; Maria Teresa Brunetti; Maria Teresa Melis; Angelo Zinzi; Zhizhong Kang; Paolo Giommi. Recognition of landslides in lunar impact craters. European Journal of Remote Sensing 2017, 51, 47 -61.
AMA StyleMarco Scaioni, Vasil Yordanov, Maria Teresa Brunetti, Maria Teresa Melis, Angelo Zinzi, Zhizhong Kang, Paolo Giommi. Recognition of landslides in lunar impact craters. European Journal of Remote Sensing. 2017; 51 (1):47-61.
Chicago/Turabian StyleMarco Scaioni; Vasil Yordanov; Maria Teresa Brunetti; Maria Teresa Melis; Angelo Zinzi; Zhizhong Kang; Paolo Giommi. 2017. "Recognition of landslides in lunar impact craters." European Journal of Remote Sensing 51, no. 1: 47-61.