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Cheng Wang
Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China

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Journal article
Published: 24 August 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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In this paper, we introduce the 2020 Gaofen Challenge and relevant scientific outcomes. The 2020 Gaofen Challenge is an international competition, which is organized by the China High-Resolution Earth Observation Conference Committee and the Aerospace Information Research Institute, Chinese Academy of Sciences and technically co-sponsored by the IEEE Geoscience and Remote Sensing Society (IEEE-GRSS) and the International Society for Photogrammetry and Remote Sensing (ISPRS). It aims at promoting the academic development of automated high-resolution earth observation image interpretation. Six independent tracks have been organized in this challenge, which cover the challenging problems in the field of object detection and semantic segmentation. With the development of convolutional neural networks, deep learning-based methods have achieved good performance on image interpretation. In this paper, we report the details and the best-performing methods presented so far in the scope of this challenge.

ACS Style

Xian Sun; Peijin Wang; Zhiyuan Yan; Wenhui Diao; Xiaonan Lu; Zhujun Yang; Yidan Zhang; Deliang Xiang; Chen Yan; Jie Guo; Bo Dang; Wei Wei; Feng Xu; Cheng Wang; Ronny Hansch; Martin Weinmann; Naoto Yokoya; Kun Fu. Automated High-resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.

AMA Style

Xian Sun, Peijin Wang, Zhiyuan Yan, Wenhui Diao, Xiaonan Lu, Zhujun Yang, Yidan Zhang, Deliang Xiang, Chen Yan, Jie Guo, Bo Dang, Wei Wei, Feng Xu, Cheng Wang, Ronny Hansch, Martin Weinmann, Naoto Yokoya, Kun Fu. Automated High-resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.

Chicago/Turabian Style

Xian Sun; Peijin Wang; Zhiyuan Yan; Wenhui Diao; Xiaonan Lu; Zhujun Yang; Yidan Zhang; Deliang Xiang; Chen Yan; Jie Guo; Bo Dang; Wei Wei; Feng Xu; Cheng Wang; Ronny Hansch; Martin Weinmann; Naoto Yokoya; Kun Fu. 2021. "Automated High-resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.

Journal article
Published: 11 August 2021 in IEEE Transactions on Intelligent Transportation Systems
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Nowadays, a large number of sensors are equipped on mobile or stationary platforms, which continuously generate geo-tagged and time-stamped readings (i.e., geo-sensory data) that contain rich information about the surrounding environment. These data have irregular space and time coordinates. To represent geo-sensory data, there have been extensive research efforts using time sequences, grid-like images, and graph signals. However, there still lacks a proper representation that can describe both the mobile and stationary geo-sensory data without the information-losing discretization in spatial and temporal dimensions. In this paper, we propose to represent massive geo-sensory data as spatio-temporal point clouds (STPC), and present STPC-Net, a novel deep neural network for processing STPC. STPC leverages the original irregular space-time coordinates, and STPC-Net captures intra-sensor and inter-sensor correlations from STPC. In this way, STPC-Net learns the key information of STPC, and overcomes challenges in data irregularity. Experiments using real-world datasets show that STPC-Net achieves state-of-the-art performance in different tasks on both mobile and stationary geo-sensory data. The source code is available at https://github.com/zhengchuanpan/STPC-Net.

ACS Style

Chuanpan Zheng; Cheng Wang; Xiaoliang Fan; Jianzhong Qi; Xu Yan. STPC-Net: Learn Massive Geo-Sensory Data as Spatio-Temporal Point Clouds. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -11.

AMA Style

Chuanpan Zheng, Cheng Wang, Xiaoliang Fan, Jianzhong Qi, Xu Yan. STPC-Net: Learn Massive Geo-Sensory Data as Spatio-Temporal Point Clouds. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-11.

Chicago/Turabian Style

Chuanpan Zheng; Cheng Wang; Xiaoliang Fan; Jianzhong Qi; Xu Yan. 2021. "STPC-Net: Learn Massive Geo-Sensory Data as Spatio-Temporal Point Clouds." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-11.

Journal article
Published: 06 August 2021 in IEEE Transactions on Intelligent Transportation Systems
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Mobile Laser Scanning (MLS) system can provide high-density and accurate 3D point clouds that enable rapid pavement crack detection for road maintenance tasks. Supervised learning-based algorithms have been proved pretty effective for handling such a large amount of inhomogeneous and unstructured point clouds. However, these algorithms often rely on a lot of annotated data, which is labor-intensive and time-consuming. This paper presents a semi-supervised point-level approach to overcome this challenge. We propose a graph-widen module to construct a reasonable graph structure for point clouds, increasing the detection performance of graph convolutional networks (GCN). The constructed graph characterizes the local features from a small amount of annotated data, avoiding information loss and dramatically reduces the dependence on annotated data. The MLS point clouds acquired by a commercial RIEGL VMX-450 system are used in this study. The experimental results demonstrate that our method outperforms the state-of-the-art point-level methods in terms of recall, F1 score, and efficiency while achieving comparable accuracy.

ACS Style

Huifang Feng; Wen Li; Zhipeng Luo; Yiping Chen; Sarah Narges Fatholahi; Ming Cheng; Cheng Wang; Jose Marcato Junior; Jonathan Li. GCN-Based Pavement Crack Detection Using Mobile LiDAR Point Clouds. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -10.

AMA Style

Huifang Feng, Wen Li, Zhipeng Luo, Yiping Chen, Sarah Narges Fatholahi, Ming Cheng, Cheng Wang, Jose Marcato Junior, Jonathan Li. GCN-Based Pavement Crack Detection Using Mobile LiDAR Point Clouds. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-10.

Chicago/Turabian Style

Huifang Feng; Wen Li; Zhipeng Luo; Yiping Chen; Sarah Narges Fatholahi; Ming Cheng; Cheng Wang; Jose Marcato Junior; Jonathan Li. 2021. "GCN-Based Pavement Crack Detection Using Mobile LiDAR Point Clouds." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-10.

Journal article
Published: 02 August 2021 in Pattern Recognition
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To recover relative camera motion accurately and robustly, establishing a set of point-to-point correspondences in the pixel space is an essential yet challenging task in computer vision. Even though multi-scale design philosophy has been used with significant success in computer vision tasks, such as object detection and semantic segmentation, learning-based image matching has not been fully exploited. In this work, we explore a scale awareness learning approach in finding pixel-level correspondences based on the intuition that keypoints need to be extracted and described on an appropriate scale. With that insight, we propose a novel scale-aware network and then develop a new fusion scheme that derives high-consistency response maps and high-precision descriptions. We also revise the Second Order Similarity Regularization (SOSR) to make it more effective for the end-to-end image matching network, which leads to significant improvement in local feature descriptions. Experimental results run on multiple datasets demonstrate that our approach performs better than state-of-the-art methods under multiple criteria.

ACS Style

Xuelun Shen; Cheng Wang; Xin Li; Yifan Peng; Zijian He; Chenglu Wen; Ming Cheng. Learning scale awareness in keypoint extraction and description. Pattern Recognition 2021, 121, 108221 .

AMA Style

Xuelun Shen, Cheng Wang, Xin Li, Yifan Peng, Zijian He, Chenglu Wen, Ming Cheng. Learning scale awareness in keypoint extraction and description. Pattern Recognition. 2021; 121 ():108221.

Chicago/Turabian Style

Xuelun Shen; Cheng Wang; Xin Li; Yifan Peng; Zijian He; Chenglu Wen; Ming Cheng. 2021. "Learning scale awareness in keypoint extraction and description." Pattern Recognition 121, no. : 108221.

Journal article
Published: 28 June 2021 in Remote Sensing
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Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory.

ACS Style

Ziyi Chen; Dilong Li; Wentao Fan; Haiyan Guan; Cheng Wang; Jonathan Li. Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images. Remote Sensing 2021, 13, 2524 .

AMA Style

Ziyi Chen, Dilong Li, Wentao Fan, Haiyan Guan, Cheng Wang, Jonathan Li. Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images. Remote Sensing. 2021; 13 (13):2524.

Chicago/Turabian Style

Ziyi Chen; Dilong Li; Wentao Fan; Haiyan Guan; Cheng Wang; Jonathan Li. 2021. "Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images." Remote Sensing 13, no. 13: 2524.

Article
Published: 18 June 2021 in Canadian Journal of Remote Sensing
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Combining Dirichlet Mixture Models (DMM) with deep learning models for road extraction is an attractive study topic. Benefiting from DMM, the manually labeling work is alleviated. However, DMM suffers from high computational complexity due to pixel by pixel computations. Also, traditional constant parameter settings of DMM may not be suitable for different target images. To address the above problems, we propose an improved DMM which embeds superpixel strategy and sparse representation into DMM. In our road extraction framework, we first use improved DMM to filter out most backgrounds. Then, a trained deep CNN model is used for further precise road area recognition. To further promote the processing speed, we also apply the superpixel scanning strategy for CNN models. We tested our method on a Shaoshan dataset and proved that our method not only can achieve better results than other compared state-of-the-art image segmentation methods, but the processing speed and accuracy of DMM are also improved.

ACS Style

Ziyi Chen; Cheng Wang; Jonathan Li; Bineng Zhong; Jixiang Du; Wentao Fan. Combined Improved Dirichlet Models and Deep Learning Models for Road Extraction from Remote Sensing Images. Canadian Journal of Remote Sensing 2021, 1 -20.

AMA Style

Ziyi Chen, Cheng Wang, Jonathan Li, Bineng Zhong, Jixiang Du, Wentao Fan. Combined Improved Dirichlet Models and Deep Learning Models for Road Extraction from Remote Sensing Images. Canadian Journal of Remote Sensing. 2021; ():1-20.

Chicago/Turabian Style

Ziyi Chen; Cheng Wang; Jonathan Li; Bineng Zhong; Jixiang Du; Wentao Fan. 2021. "Combined Improved Dirichlet Models and Deep Learning Models for Road Extraction from Remote Sensing Images." Canadian Journal of Remote Sensing , no. : 1-20.

Journal article
Published: 07 June 2021 in Information Sciences
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Point clouds and models with semantic information facilitate various indoor automation, ranging from indoor robotics to emergency responses. Studies are currently being conducted on semantic labeling and modeling based on offline mapped point clouds, in which, the performance is strongly limited by the mapping process. To address this issue, we propose a framework to cooperatively perform the three tasks of semantic labeling, mapping, and 3D modeling of point clouds. First, our framework uses a deep-learning-assisted method to perform frame-level point cloud semantic labeling. Subsequently, point cloud frames with semantic labels are used to extract the structural planes of buildings, followed by the generation of line structures from the planes. Then, these frames are used to estimate the initial poses of a 3D sensor for data collection. In the subsequent pose optimization process, the initial poses are optimized under the constraints of the structural planes. Finally, the optimized poses are used to integrate semantic frames and line structures to generate a point cloud map and 3D line model of buildings. The experimental results show that the proposed method achieves better results than the state-of-the-art methods that separately perform one of the two tasks.

ACS Style

Chenglu Wen; Jinbin Tan; Fashuai Li; Chongrong Wu; Yitai Lin; Zhiyong Wang; Cheng Wang. Cooperative indoor 3D mapping and modeling using LiDAR data. Information Sciences 2021, 574, 192 -209.

AMA Style

Chenglu Wen, Jinbin Tan, Fashuai Li, Chongrong Wu, Yitai Lin, Zhiyong Wang, Cheng Wang. Cooperative indoor 3D mapping and modeling using LiDAR data. Information Sciences. 2021; 574 ():192-209.

Chicago/Turabian Style

Chenglu Wen; Jinbin Tan; Fashuai Li; Chongrong Wu; Yitai Lin; Zhiyong Wang; Cheng Wang. 2021. "Cooperative indoor 3D mapping and modeling using LiDAR data." Information Sciences 574, no. : 192-209.

Journal article
Published: 25 May 2021 in Computers in Biology and Medicine
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Organoid, an in vitro 3D culture, has extremely high similarity with its source organ or tissue, which creates a model in vitro that simulates the in vivo environment. Organoids have been extensively studied in cell biology, precision medicine, drug toxicity, efficacy tests, etc., which have been proven to have high research value. Periodic observation of organoids in microscopic images to obtain morphological or growth characteristics is essential for organoid research. It is difficult and time-consuming to perform manual screens for organoids, but there is no better solution in the prior art. In this paper, we established the first high-throughput organoid image dataset for organoids detection and tracking, which experienced experts annotate in detail. Moreover, we propose a novel deep neural network (DNN) that effectively detects organoids and dynamically tracks them throughout the entire culture. We divided our solution into two steps: First, the high-throughput sequential images are processed frame by frame to detect all organoids; Second, the similarities of the organoids in the adjacent frames are computed, and the organoids on the adjacent frames are matched in pairs. With the help of our proposed dataset, our model achieves organoids detection and tracking with fast speed and high accuracy, effectively reducing the burden on researchers. To our knowledge, this is the first exploration of applying deep learning to organoid tracking tasks. Experiments have demonstrated that our proposed method achieved satisfactory results on organoid detection and tracking, verifying the great potential of deep learning technology in this field.

ACS Style

Xuesheng Bian; Gang Li; Cheng Wang; Weiquan Liu; Xiuhong Lin; Zexin Chen; Mancheung Cheung; Xiongbiao Luo. A deep learning model for detection and tracking in high-throughput images of organoid. Computers in Biology and Medicine 2021, 134, 104490 .

AMA Style

Xuesheng Bian, Gang Li, Cheng Wang, Weiquan Liu, Xiuhong Lin, Zexin Chen, Mancheung Cheung, Xiongbiao Luo. A deep learning model for detection and tracking in high-throughput images of organoid. Computers in Biology and Medicine. 2021; 134 ():104490.

Chicago/Turabian Style

Xuesheng Bian; Gang Li; Cheng Wang; Weiquan Liu; Xiuhong Lin; Zexin Chen; Mancheung Cheung; Xiongbiao Luo. 2021. "A deep learning model for detection and tracking in high-throughput images of organoid." Computers in Biology and Medicine 134, no. : 104490.

Journal article
Published: 13 May 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Extracting the power lines and pylons automatically and accurately from airborne LiDAR data is a critical step in inspecting the routine power line, especially in the remote mountainous areas. However, challenges arise in using existing methods to extract the targets from large scenarios of remote mountainous areas since the terrain is undulating, and the features are difficult to distinguish. In this article, to overcome these challenges, we propose a graph convolutional network (GCN)-based method to extract power lines and pylons from Airborne LiDAR point clouds. First, data augmentation and near-ground filtering methods are developed to overcome the problems of insufficient and imbalanced samples in the LiDAR data. Then, a GCN-based framework is proposed to extract the power lines and pylons, which consist of two main modules, i.e., the neighborhood dimension information (NDI) module and the neighborhood geometry information aggregation (NGIA) module. These two modules are designed to strengthen the model's ability to portray local geometric details. Besides, an attention fusion module is investigated to further improve the NDI and NGIA features. Finally, a line structure constraint algorithm is proposed to identify individual power lines, where the power corridor is reconstructed using a polynomial-based algorithm. Numerical experiments are conducted based on two different power line scenarios acquired in mountainous areas. The results demonstrate the superior performances of the proposed method over several existing algorithms, where the F₁ score and quality of the power line are 99.3% and 98.6%, and the results of the pylon are 96% and 92.4%, respectively. The identification rate of power line identification is above 98%.

ACS Style

Wen Li; Zhipeng Luo; Zhenlong Xiao; Yiping Chen; Cheng Wang; Jonathan Li. A GCN-Based Method for Extracting Power Lines and Pylons From Airborne LiDAR Data. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -14.

AMA Style

Wen Li, Zhipeng Luo, Zhenlong Xiao, Yiping Chen, Cheng Wang, Jonathan Li. A GCN-Based Method for Extracting Power Lines and Pylons From Airborne LiDAR Data. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-14.

Chicago/Turabian Style

Wen Li; Zhipeng Luo; Zhenlong Xiao; Yiping Chen; Cheng Wang; Jonathan Li. 2021. "A GCN-Based Method for Extracting Power Lines and Pylons From Airborne LiDAR Data." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.

Journal article
Published: 24 April 2021 in International Journal of Applied Earth Observation and Geoinformation
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Road extraction from optical remote sensing images has many important application scenarios, such as navigation, automatic driving and road network planning, etc. Current deep learning based models have achieved great successes in road extraction. Most deep learning models improve abilities rely on using deeper layers, resulting to the obese of the trained model. Besides, the training of a deep model is also difficult, and may be easy to fall into over fitting. Thus, this paper studies to improve the performance through combining multiple lightweight models. However, in fact multiple isolated lightweight models may perform worse than a deeper and larger model. The reason is that those models are trained isolated. To solve the above problem, we propose an Adaboost-like End-To-End Multiple Lightweight U-Nets model (AEML U-Nets) for road extraction. Our model consists of multiple lightweight U-Net parts. Each output of prior U-Net is as the input of next U-Net. We design our model as multiple-objective optimization problem to jointly train all the U-Nets. The approach is tested on two open datasets (LRSNY and Massachusetts) and Shaoshan dataset. Experimental results prove that our model has better performance compared with other state-of-the-art semantic segmentation methods.

ACS Style

Ziyi Chen; Cheng Wang; Jonathan Li; Wentao Fan; Jixiang Du; Bineng Zhong. Adaboost-like End-to-End multiple lightweight U-nets for road extraction from optical remote sensing images. International Journal of Applied Earth Observation and Geoinformation 2021, 100, 102341 .

AMA Style

Ziyi Chen, Cheng Wang, Jonathan Li, Wentao Fan, Jixiang Du, Bineng Zhong. Adaboost-like End-to-End multiple lightweight U-nets for road extraction from optical remote sensing images. International Journal of Applied Earth Observation and Geoinformation. 2021; 100 ():102341.

Chicago/Turabian Style

Ziyi Chen; Cheng Wang; Jonathan Li; Wentao Fan; Jixiang Du; Bineng Zhong. 2021. "Adaboost-like End-to-End multiple lightweight U-nets for road extraction from optical remote sensing images." International Journal of Applied Earth Observation and Geoinformation 100, no. : 102341.

Journal article
Published: 30 March 2021 in IEEE Transactions on Intelligent Transportation Systems
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High-Accuracy and high-efficiency 3-D sensing and associated data processing techniques are urgently needed for today’s roadway inventory, infrastructure health monitoring, autonomous driving, connected vehicles, urban modeling, and smart cities. 3D geospatial data acquired by digital photogrammetry or laser scanning or LiDAR systems have become one of the most critical data sources to support the above-mentioned applications. While progress has been made to applying 3D sensory data to those applications related to intelligent transportation systems (ITS), such as road network extraction, platform localization, obstacle avoidance, high-definition map generation, and transportation infrastructure inventory, many essential questions remain regarding the processing and understanding such massive 3D datasets in ITS-related applications. The authors have selected four articles for review in this Special issue. A summary of these articles is outlined below.

ACS Style

Chenglu Wen; Ayman F. Habib; Jonathan Li; Charles K. Toth; Cheng Wang; Hongchao Fan. Special Issue on 3D Sensing in Intelligent Transportation. IEEE Transactions on Intelligent Transportation Systems 2021, 22, 1947 -1949.

AMA Style

Chenglu Wen, Ayman F. Habib, Jonathan Li, Charles K. Toth, Cheng Wang, Hongchao Fan. Special Issue on 3D Sensing in Intelligent Transportation. IEEE Transactions on Intelligent Transportation Systems. 2021; 22 (4):1947-1949.

Chicago/Turabian Style

Chenglu Wen; Ayman F. Habib; Jonathan Li; Charles K. Toth; Cheng Wang; Hongchao Fan. 2021. "Special Issue on 3D Sensing in Intelligent Transportation." IEEE Transactions on Intelligent Transportation Systems 22, no. 4: 1947-1949.

Journal article
Published: 12 March 2021 in IEEE Geoscience and Remote Sensing Letters
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Automatic building extraction from remote sensing imagery is crucial to urban construction and management. To address the main challenges of diverse building scale and appearance, this letter proposes an automatic building instance extraction method based on an improved hybrid task cascade (HTC). Our method consists of three components by obtaining high-resolution representation, defining guided anchor, and forming focal loss to boost the adaptability of automatic building instance extraction. Comprehensive experimental results on WHU aerial building data set demonstrated that compared with the mainstream Mask R-CNN method, our method increased AP and AR in bounding box branch and mask branch by 9.8%-6.5% and 10.7%-8.0% respectively, especially APS and APL in the two branches by 10.1%-6.9% and 3.4%-2.4%, respectively. We evaluated the effectiveness and complexity of these components separately and discussed the universality and practicability of deep learning method in automatic building extraction.

ACS Style

Xiaoxue Liu; Yiping Chen; Mingqiang Wei; Cheng Wang; Wesley Nunes Goncalves; Jose Marcato Junior; Jonathan Li. Building Instance Extraction Method Based on Improved Hybrid Task Cascade. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.

AMA Style

Xiaoxue Liu, Yiping Chen, Mingqiang Wei, Cheng Wang, Wesley Nunes Goncalves, Jose Marcato Junior, Jonathan Li. Building Instance Extraction Method Based on Improved Hybrid Task Cascade. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.

Chicago/Turabian Style

Xiaoxue Liu; Yiping Chen; Mingqiang Wei; Cheng Wang; Wesley Nunes Goncalves; Jose Marcato Junior; Jonathan Li. 2021. "Building Instance Extraction Method Based on Improved Hybrid Task Cascade." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.

Journal article
Published: 26 February 2021 in ISPRS Journal of Photogrammetry and Remote Sensing
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As a dynamic and essential component in the road environment of urban scenarios, vehicles are the most popular investigation targets. To monitor their behavior and extract their geometric characteristics, an accurate and instant measurement of vehicles plays a vital role in traffic and transportation fields. Point clouds acquired from the mobile laser scanning (MLS) system deliver 3D information of road scenes with unprecedented detail. They have proven to be an adequate data source in the fields of intelligent transportation and autonomous driving, especially for extracting vehicles. However, acquired 3D point clouds of vehicles from MLS systems are inevitably incomplete due to object occlusion or self-occlusion. To tackle this problem, we proposed a neural network to synthesize complete, dense, and uniform point clouds for vehicles from MLS data, named Vehicle Points Completion-Net (VPC-Net). In this network, we introduce a new encoder module to extract global features from the input instance, consisting of a spatial transformer network and point feature enhancement layer. Moreover, a new refiner module is also presented to preserve the vehicle details from inputs and refine the complete outputs with fine-grained information. Given sparse and partial point clouds as inputs, the network can generate complete and realistic vehicle structures and keep the fine-grained details from the partial inputs. We evaluated the proposed VPC-Net in different experiments using synthetic and real-scan datasets and applied the results to 3D vehicle monitoring tasks. Quantitative and qualitative experiments demonstrate the promising performance of the proposed VPC-Net and show state-of-the-art results.

ACS Style

Yan Xia; Yusheng Xu; Cheng Wang; Uwe Stilla. VPC-Net: Completion of 3D vehicles from MLS point clouds. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 174, 166 -181.

AMA Style

Yan Xia, Yusheng Xu, Cheng Wang, Uwe Stilla. VPC-Net: Completion of 3D vehicles from MLS point clouds. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 174 ():166-181.

Chicago/Turabian Style

Yan Xia; Yusheng Xu; Cheng Wang; Uwe Stilla. 2021. "VPC-Net: Completion of 3D vehicles from MLS point clouds." ISPRS Journal of Photogrammetry and Remote Sensing 174, no. : 166-181.

Journal article
Published: 10 February 2021 in IEEE Transactions on Intelligent Transportation Systems
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This paper proposes a new 3D multi-object tracker to more robustly track objects that are temporarily missed by detectors. Our tracker can better leverage object features for 3D Multi-Object Tracking (MOT) in point clouds. The proposed tracker is based on a novel data association scheme guided by prediction confidence, and it consists of two key parts. First, we design a new predictor that employs a constant acceleration (CA) motion model to estimate future positions, and outputs a prediction confidence to guide data association through increased awareness of detection quality. Second, we introduce a new aggregated pairwise cost to exploit features of objects in point clouds for faster and more accurate data association. The proposed cost consists of geometry, appearance and motion components. Specifically, we formulate the geometry cost using resolutions (lengths, widths and heights), centroids, and orientations of 3D bounding boxes (BBs), the appearance cost using appearance features from the deep learning-based detector backbone network, and the motion cost by associating different motion vectors. Extensive multi-object tracking experiments on the KITTI tracking benchmark demonstrated that our method outperforms, by a large margin, the state-of-the-art methods in both tracking accuracy and speed.

ACS Style

Hai Wu; Wenkai Han; Chenglu Wen; Xin Li; Cheng Wang. 3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -10.

AMA Style

Hai Wu, Wenkai Han, Chenglu Wen, Xin Li, Cheng Wang. 3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-10.

Chicago/Turabian Style

Hai Wu; Wenkai Han; Chenglu Wen; Xin Li; Cheng Wang. 2021. "3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-10.

Journal article
Published: 01 February 2021 in IEEE Geoscience and Remote Sensing Letters
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We present a registration strategy based on a planar primitive group for indoor environments between a large-scale point cloud and a small-scale point cloud, providing a localization solution that is fully independent of prior information about the initial positions of the two point cloud coordinate systems. The algorithm first divides the point cloud into planes by region growing and then refines the plane boundaries by local k-means clustering. The planes are grouped to obtain planar primitive groups that are used to infer potential matching regions for an effective coarse registration and then fine-tuning. The algorithm is applied for autonomous vehicle localization in underground garages. Evaluation on four datasets shows that the algorithm can provide decimeter-level localization accuracy in seconds.

ACS Style

Lili Lin; Wenwen Zhang; Ming Cheng; Chenglu Wen; Cheng Wang. Planar Primitive Group-Based Point Cloud Registration for Autonomous Vehicle Localization in Underground Parking Lots. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.

AMA Style

Lili Lin, Wenwen Zhang, Ming Cheng, Chenglu Wen, Cheng Wang. Planar Primitive Group-Based Point Cloud Registration for Autonomous Vehicle Localization in Underground Parking Lots. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.

Chicago/Turabian Style

Lili Lin; Wenwen Zhang; Ming Cheng; Chenglu Wen; Cheng Wang. 2021. "Planar Primitive Group-Based Point Cloud Registration for Autonomous Vehicle Localization in Underground Parking Lots." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.

Journal article
Published: 22 January 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Automatic road extraction from remote sensing images plays an important role for navigation, intelligent transportation, and road network update, etc. Convolutional neural network (CNN)-based methods have presented many achievements for road extraction from remote sensing images. CNN-based methods require a large dataset with high quality labels for model training. However, there is still few standard and large dataset, which is specially designed for road extraction from optical remote sensing images. Besides, the existing end-to-end CNN models for road extraction from remote sensing images are usually with symmetric structure, studying on asymmetric structure between encoding and decoding is rare. To address the above problems, this article first provides a publicly available dataset LRSNY for road extraction from optical remote sensing images with manually labelled labels. Second, we propose a reconstruction bias U-Net for road extraction from remote sensing images. In our model, we increase the decoding branches to obtain multiple semantic information from different upsamplings. Experimental results show that our method achieves better performance compared with other six state-of-the-art segmentation models when testing on our LRSNY dataset. We also test on Massachusetts and Shaoshan datasets. The good performances on the two datasets further prove the effectiveness of our method.

ACS Style

Ziyi Chen; Cheng Wang; Jonathan Li; Nianci Xie; Yan Han; Jixiang Du. Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 2284 -2294.

AMA Style

Ziyi Chen, Cheng Wang, Jonathan Li, Nianci Xie, Yan Han, Jixiang Du. Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 ():2284-2294.

Chicago/Turabian Style

Ziyi Chen; Cheng Wang; Jonathan Li; Nianci Xie; Yan Han; Jixiang Du. 2021. "Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. : 2284-2294.

Journal article
Published: 16 January 2021 in ISPRS Journal of Photogrammetry and Remote Sensing
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In recent years, deep learning-based algorithms have brought great improvements to rigid object detection. In addition to rigid objects, remote sensing images also contain many complex composite objects, such as sewage treatment plants, golf courses, and airports, which have neither a fixed shape nor a fixed size. In this paper, we validate through experiments that the results of existing methods in detecting composite objects are not satisfying enough. Therefore, we propose a unified part-based convolutional neural network (PBNet), which is specifically designed for composite object detection in remote sensing imagery. PBNet treats a composite object as a group of parts and incorporates part information into context information to improve composite object detection. Correct part information can guide the prediction of a composite object, thus solving the problems caused by various shapes and sizes. To generate accurate part information, we design a part localization module to learn the classification and localization of part points using bounding box annotation only. A context refinement module is designed to generate more discriminative features by aggregating local context information and global context information, which enhances the learning of part information and improve the ability of feature representation. We selected three typical categories of composite objects from a public dataset to conduct experiments to verify the detection performance and generalization ability of our method. Meanwhile, we build a more challenging dataset about a typical kind of complex composite objects, i.e., sewage treatment plants. It refers to the relevant information from authorities and experts. This dataset contains sewage treatment plants in seven cities in the Yangtze valley, covering a wide range of regions. Comprehensive experiments on two datasets show that PBNet surpasses the existing detection algorithms and achieves state-of-the-art accuracy.

ACS Style

Xian Sun; Peijin Wang; Cheng Wang; Yingfei Liu; Kun Fu. PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 173, 50 -65.

AMA Style

Xian Sun, Peijin Wang, Cheng Wang, Yingfei Liu, Kun Fu. PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 173 ():50-65.

Chicago/Turabian Style

Xian Sun; Peijin Wang; Cheng Wang; Yingfei Liu; Kun Fu. 2021. "PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery." ISPRS Journal of Photogrammetry and Remote Sensing 173, no. : 50-65.

Journal article
Published: 12 January 2021 in ISPRS Journal of Photogrammetry and Remote Sensing
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LiDAR-based localization in a city-scale map is a fundamental question in autonomous driving research. As a reasonable localization scheme, the localization can be performed by global retrieval (that suggests potential candidates from the database) followed by geometric registration (that obtains an accurate relative pose). In this work, we develop a novel end-to-end, deep multi-task network that simultaneously performs global retrieval and geometric registration for LiDAR-based localization. Both retrieval and registration are formulated and solved as regression problems, and they can be deployed independently during inference time. We also design two mechanisms to enhance our multi-task regression network’s performance: residual connections for point clouds and a new loss function with learnable parameters. To alleviate the common phenomenon of vanishing gradients in neural networks, we employ residual connections to support constructing a deeper network effectively. At the same time, to solve the problem of huge differences in scale and units between different tasks, we propose a loss function that can automatically balance multi-tasks. Experiments on two public benchmarks validate the state-of-the-art performance of our algorithm in large-scale LiDAR-based localization.

ACS Style

Shangshu Yu; Cheng Wang; Zenglei Yu; Xin Li; Ming Cheng; Yu Zang. Deep regression for LiDAR-based localization in dense urban areas. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 172, 240 -252.

AMA Style

Shangshu Yu, Cheng Wang, Zenglei Yu, Xin Li, Ming Cheng, Yu Zang. Deep regression for LiDAR-based localization in dense urban areas. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 172 ():240-252.

Chicago/Turabian Style

Shangshu Yu; Cheng Wang; Zenglei Yu; Xin Li; Ming Cheng; Yu Zang. 2021. "Deep regression for LiDAR-based localization in dense urban areas." ISPRS Journal of Photogrammetry and Remote Sensing 172, no. : 240-252.

Article
Published: 02 January 2021 in Canadian Journal of Remote Sensing
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Due to the wide distribution of crosswalks over the road nets, the finding of impaired crosswalk marks is usually long-time delayed, which may put crosswalk pedestrians into danger. To reduce the repairing cost and improve the finding speed of damaged crosswalks, this paper uses remote sensing images to automatically detect crosswalks. The detection results can be used for further examination of crosswalks. However, the detection of crosswalks from remote sensing images suffers from serious interferes of many other kinds of ground targets. Besides, there are rare openly available datasets for the research of crosswalk detection from remote sensing images. To conquer the above problems, this study provides an openly available dataset for the research of crosswalk detection. To improve the robustness, we propose a crosswalk detection framework which uses a U-Net based road area guidance. First, we use CNN models to detect crosswalks. Then, we use U-Net to extract potential road areas. Third, we propose a mixture classification strategy which combines the detection confidence and potential road area guidance for final crosswalk detection. Experimental results show that the road area guidance for crosswalks’ detection is effective and can improve the detection performance.

ACS Style

Ziyi Chen; Ruixiang Luo; Jonathan Li; Jixiang Du; Cheng Wang. U-Net Based Road Area Guidance for Crosswalks Detection from Remote Sensing Images. Canadian Journal of Remote Sensing 2021, 47, 83 -99.

AMA Style

Ziyi Chen, Ruixiang Luo, Jonathan Li, Jixiang Du, Cheng Wang. U-Net Based Road Area Guidance for Crosswalks Detection from Remote Sensing Images. Canadian Journal of Remote Sensing. 2021; 47 (1):83-99.

Chicago/Turabian Style

Ziyi Chen; Ruixiang Luo; Jonathan Li; Jixiang Du; Cheng Wang. 2021. "U-Net Based Road Area Guidance for Crosswalks Detection from Remote Sensing Images." Canadian Journal of Remote Sensing 47, no. 1: 83-99.

Journal article
Published: 24 November 2020 in IEEE Geoscience and Remote Sensing Letters
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Rapid and accurate enhancement of road boundaries from terrestrial laser scanning (TLS) 3-D point clouds has been a challenging task in road infrastructure inventory. To address the challenge with a lack of ability to enhance object boundaries when the supervoxel number is less, this letter proposes a novel supervoxel segmentation algorithm framework for enhancing road boundaries from 3-D point clouds. First, we utilize radius k nearest-neighbor search method to obtain the neighborhood information after partitioning points on octrees with seed points. Second, the iterative weighted least square algorithm and spatial structure judgment are used to segment point clouds based on seed points. Finally, an update method to adjust the supervoxel centroids is applied with surrounding information in the first part. To verify the excellent performance, we tested the proposed method on two publicly large-scale point clouds benchmarks--IQmulus and TerraMobilita (IQTM) and Semantic 3-D. The experimental results demonstrate that our approach achieved approximately 48.98% and 68.41% boundary recall higher than two existing classical methods in the street scene, and our running time is feasible and effective.

ACS Style

Zhengchuan Sha; Yiping Chen; Yangbin Lin; Cheng Wang; Jose Marcato; Jonathan Li. A Supervoxel Approach to Road Boundary Enhancement From 3-D LiDAR Point Clouds. IEEE Geoscience and Remote Sensing Letters 2020, PP, 1 -5.

AMA Style

Zhengchuan Sha, Yiping Chen, Yangbin Lin, Cheng Wang, Jose Marcato, Jonathan Li. A Supervoxel Approach to Road Boundary Enhancement From 3-D LiDAR Point Clouds. IEEE Geoscience and Remote Sensing Letters. 2020; PP (99):1-5.

Chicago/Turabian Style

Zhengchuan Sha; Yiping Chen; Yangbin Lin; Cheng Wang; Jose Marcato; Jonathan Li. 2020. "A Supervoxel Approach to Road Boundary Enhancement From 3-D LiDAR Point Clouds." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.