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Jiasong Zhu
Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources, Shenzhen University, Shenzhen 518060, China

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Journal article
Published: 18 June 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Due to the detailed spectral information through hundreds of narrow spectral bands provided by hyperspectral image (HSI) data, it can be employed to accurately classify diverse materials of interest, which is one of the core applications of hyperspectral remote sensing technology. In recent years, with the rapid development of deep learning, convolutional neural networks (CNNs) have been successfully applied in many fields, including HSI classification. However, the random gradient descent-based parameter updating scheme is too general and leading to the inefficiency of CNN models. Moreover, the high dimensionality and limited training samples of HSI data also exacerbate the overfitting problem. To tackle these issues, in this article, a novel deep network with multilayer and multibranch architecture, named 3-D Gabor CNN (3DG-CNN), is proposed for HSI classification. More precisely, since the predefined 3-D Gabor filters in multiple scales and orientations could well characterize the internal spatial-spectral structure of HSI data from various perspectives, the 3-D Gabor-modulated kernels (3-D GMKs) are employed to replace the random initialization kernels. Moreover, the specially designed multibranch architecture enables the network to better integrating the scalable property of 3-D Gabor filters; thus, the representative ability and robustness of the extracted features can be greatly improved. Alternatively, the number of network parameters is substantially reduced due to the incorporation of 3-D Gabor modulation, relieving the training complexity and also alleviating the training process from overfitting. Experimental results on four real HSI datasets (including two newly released ones in the literature) have demonstrated that the proposed 3DG-CNN model can achieve better performance than several widely used machine-learning-based and deep-learning-based approaches. For the sake of reproducibility, the codes of the proposed 3DG-CNN model are available at http://jiasen.tech/papers/.

ACS Style

Sen Jia; Jianhui Liao; Meng Xu; Yan Li; Jiasong Zhu; Weiwei Sun; Xiuping Jia; Qingquan Li. 3-D Gabor Convolutional Neural Network for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -16.

AMA Style

Sen Jia, Jianhui Liao, Meng Xu, Yan Li, Jiasong Zhu, Weiwei Sun, Xiuping Jia, Qingquan Li. 3-D Gabor Convolutional Neural Network for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-16.

Chicago/Turabian Style

Sen Jia; Jianhui Liao; Meng Xu; Yan Li; Jiasong Zhu; Weiwei Sun; Xiuping Jia; Qingquan Li. 2021. "3-D Gabor Convolutional Neural Network for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-16.

Journal article
Published: 04 September 2020 in IEEE Transactions on Vehicular Technology
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A new method is proposed for selecting Access Points (APs) with propagation direction combination based on Eight-Diagram for indoor localization in indoor environments, which can not only improve positioning accuracy, but also reduce computational complexity by using fewer APs. With propagation direction combination based on eight basic directions, for instance, east, south, west, north, southeast, northeast, southwest and northwest, provided by the Eight-Diagram, the proposed AP selection algorithm can be applied to indoor scenarios where Wi-Fi RSSI is corrupted by multipath interference. Experiments were conducted and the results demonstrate that the proposed AP selection algorithm achieves an accuracy considerably better than the WKNN, MaxMean, InfoGain and PCA methods. Due to the use of fewer APs, the proposed algorithm has lower computational complexity than the MaxMean and InfoGain algorithms, and equivalent with the PCA algorithm.

ACS Style

Weixing Xue; Kegen Yu; Qingquan Li; Baoding Zhou; Jiasong Zhu; Yuwei Chen; Weining Qiu; Xianghong Hua; Wei Ma; Zhipeng Chen. Eight-Diagram Based Access Point Selection Algorithm for Indoor Localization. IEEE Transactions on Vehicular Technology 2020, 69, 13196 -13205.

AMA Style

Weixing Xue, Kegen Yu, Qingquan Li, Baoding Zhou, Jiasong Zhu, Yuwei Chen, Weining Qiu, Xianghong Hua, Wei Ma, Zhipeng Chen. Eight-Diagram Based Access Point Selection Algorithm for Indoor Localization. IEEE Transactions on Vehicular Technology. 2020; 69 (11):13196-13205.

Chicago/Turabian Style

Weixing Xue; Kegen Yu; Qingquan Li; Baoding Zhou; Jiasong Zhu; Yuwei Chen; Weining Qiu; Xianghong Hua; Wei Ma; Zhipeng Chen. 2020. "Eight-Diagram Based Access Point Selection Algorithm for Indoor Localization." IEEE Transactions on Vehicular Technology 69, no. 11: 13196-13205.

Journal article
Published: 27 April 2020 in Transportation Research Part A: Policy and Practice
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Inferring travel modes of travelers in the city is important to transportation planning and infrastructure design. Based on the distribution of travel modes, transportation engineers could provide some proper strategies to reduce traffic congestion and air pollution. With advanced sensing techniques, it is possible to collect high-resolution GPS trajectory data of travelers and we can infer travel modes using some popular machine learning methods. One of the difficult tasks facing the application of machine learning especially deep learning in travel mode detection is the lack of large, labeled dataset, because to label the trajectory data is expensive and time-consuming. Moreover, samples of different travel modes are always unbalanced. Accordingly, in this paper, we take advantage of the generative model and the Convolutional Neural Networks (CNN) to develop a hybrid travel modes detection model using less labeled trajectory data. Our key contribution is the utilization of a generative adversarial network (GAN) to artificially create some training samples in such a way that it not only increases the required sample size but balances the dataset to improve the accuracy of the detection model. Furthermore, CNN is applied to extract deep features of trajectory data, and then to classify the travel modes. The results show that the highest accuracy (86.70%) can be achieved by the proposed model. In particular, the proposed method can improve the detection accuracy of bus and driving modes because it can solve the small sample size problem. Moreover, the large sample size can provide an opportunity to develop some advanced deep learning models in future studies.

ACS Style

Linchao Li; Jiasong Zhu; Hailong Zhang; Huachun Tan; Bowen Du; Bin Ran. Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data. Transportation Research Part A: Policy and Practice 2020, 136, 282 -292.

AMA Style

Linchao Li, Jiasong Zhu, Hailong Zhang, Huachun Tan, Bowen Du, Bin Ran. Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data. Transportation Research Part A: Policy and Practice. 2020; 136 ():282-292.

Chicago/Turabian Style

Linchao Li; Jiasong Zhu; Hailong Zhang; Huachun Tan; Bowen Du; Bin Ran. 2020. "Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data." Transportation Research Part A: Policy and Practice 136, no. : 282-292.

Journal article
Published: 24 February 2020 in IEEE Access
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Most existing sewer pipeline condition assessment methods determine the presence and types of faults via examination of videos, which is a time-consuming and labor-intensive process. A few automatic methods based on image processing techniques can be used to detect specific faults. However, these methods have limitations due to the presence of unpredictable sewer pipeline fault patterns. Deep learning methods have also been applied to sewer pipeline fault detection. However, these methods require a large amount of annotated data to obtain reliable results. In this paper, we propose a fault detection method that applies unsupervised machine learning based anomaly detection algorithms with feature extraction to videos recorded by new sewer pipeline visual inspection equipment. The recorded videos are regarded as sequence signals, which are converted into feature vectors, followed by application of an anomaly detection algorithm. Unlike existing methods, the proposed method is computationally efficient as it does not require an annotated fault sample database for training fault detection models. We evaluate various anomaly detection algorithms and feature combinations on real sewer pipeline data collected in Shenzhen, with an overall accuracy result of above 90%. The proposed method provides a new and fast technique for surveying urban sewer pipelines, and to facilitate further research in this area, we have made the code and data used in this paper publicly available.

ACS Style

Xu Fang; Wenhao Guo; Qingquan Li; Jiasong Zhu; Zhipeng Chen; Jianwei Yu; Baoding Zhou; Haokun Yang. Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences. IEEE Access 2020, 8, 39574 -39586.

AMA Style

Xu Fang, Wenhao Guo, Qingquan Li, Jiasong Zhu, Zhipeng Chen, Jianwei Yu, Baoding Zhou, Haokun Yang. Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences. IEEE Access. 2020; 8 (99):39574-39586.

Chicago/Turabian Style

Xu Fang; Wenhao Guo; Qingquan Li; Jiasong Zhu; Zhipeng Chen; Jianwei Yu; Baoding Zhou; Haokun Yang. 2020. "Sewer Pipeline Fault Identification Using Anomaly Detection Algorithms on Video Sequences." IEEE Access 8, no. 99: 39574-39586.

Journal article
Published: 11 July 2019 in IEEE Transactions on Geoscience and Remote Sensing
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Hyperspectral remote sensing imagery provides valuable and rich information to distinguish the characteristics of materials. However, this advantage of hyperspectral imagery often encounters the problem of a limited amount of training samples, which is caused by the difficulty of manually labeling. Fortunately, the spatial distribution of surface objects can be integrated with the spectral signature to improve the discriminative ability. In this paper, a 3-D Gaussian-Gabor feature extraction and selection framework has been proposed for hyperspectral image classification. First, a bank of 3-D Gaussian-Gabor filters are convolved with the concatenated data of both extended multi-attribute profile (EMAP) features and raw hyperspectral data. Second, an improved fast density peak clustering (IFDPC) method is introduced to select the most representative features from each extracted 3-D Gaussian-Gabor feature cube. Finally, the retained features are combined together to accomplish the classification task. The proposed method is thus named as GG-IFDPC. Three real hyperspectral imagery data sets have been utilized, and the experiments demonstrate the advantages of the proposed GG-IFDPC approach over the compared ones.

ACS Style

Sen Jia; Jiayue Zhuang; Lin Deng; Jiasong Zhu; Meng Xu; Jun Zhou; Xiuping Jia. 3-D Gaussian–Gabor Feature Extraction and Selection for Hyperspectral Imagery Classification. IEEE Transactions on Geoscience and Remote Sensing 2019, 57, 8813 -8826.

AMA Style

Sen Jia, Jiayue Zhuang, Lin Deng, Jiasong Zhu, Meng Xu, Jun Zhou, Xiuping Jia. 3-D Gaussian–Gabor Feature Extraction and Selection for Hyperspectral Imagery Classification. IEEE Transactions on Geoscience and Remote Sensing. 2019; 57 (11):8813-8826.

Chicago/Turabian Style

Sen Jia; Jiayue Zhuang; Lin Deng; Jiasong Zhu; Meng Xu; Jun Zhou; Xiuping Jia. 2019. "3-D Gaussian–Gabor Feature Extraction and Selection for Hyperspectral Imagery Classification." IEEE Transactions on Geoscience and Remote Sensing 57, no. 11: 8813-8826.

Journal article
Published: 02 July 2019 in IEEE Transactions on Neural Networks and Learning Systems
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A 3-D Gabor wavelet provides an effective way to obtain the spectral-spatial-fused features for hyperspectral image, which has shown advantageous performance for material classification and recognition. In this paper, instead of separately employing the Gabor magnitude and phase features, which, respectively, reflect the intensity and variation of surface materials in local area, a cascade superpixel regularized Gabor feature fusion (CSRGFF) approach has been proposed. First, the Gabor filters with particular orientation are utilized to obtain Gabor features (including magnitude and phase) from the original hyperspectral image. Second, a support vector machine (SVM)-based probability representation strategy is developed to fully exploit the decision information in SVM output, and the achieved confidence score can make the following fusion with Gabor phase more effective. Meanwhile, the quadrant bit coding and Hamming distance metric are applied to encode the Gabor phase features and measure sample similarity in sequence. Third, the carefully defined characteristics of two kinds of features are directly combined together without any weighting operation to describe the weight of samples belonging to each class. Finally, a series of superpixel graphs extracted from the raw hyperspectral image with different numbers of superpixels are employed to successively regularize the weighting cube from over-segmentation to under-segmentation, and the classification performance gradually improves with the decrease in the number of superpixels in the regularization procedure. Four widely used real hyperspectral images have been conducted, and the experimental results constantly demonstrate the superiority of our CSRGFF approach over several state-of-the-art methods.

ACS Style

Sen Jia; Zhijie Lin; Bin Deng; Jiasong Zhu; Qingquan Li. Cascade Superpixel Regularized Gabor Feature Fusion for Hyperspectral Image Classification. IEEE Transactions on Neural Networks and Learning Systems 2019, 31, 1638 -1652.

AMA Style

Sen Jia, Zhijie Lin, Bin Deng, Jiasong Zhu, Qingquan Li. Cascade Superpixel Regularized Gabor Feature Fusion for Hyperspectral Image Classification. IEEE Transactions on Neural Networks and Learning Systems. 2019; 31 (5):1638-1652.

Chicago/Turabian Style

Sen Jia; Zhijie Lin; Bin Deng; Jiasong Zhu; Qingquan Li. 2019. "Cascade Superpixel Regularized Gabor Feature Fusion for Hyperspectral Image Classification." IEEE Transactions on Neural Networks and Learning Systems 31, no. 5: 1638-1652.

Journal article
Published: 04 June 2019 in IEEE Transactions on Geoscience and Remote Sensing
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In virtue of the spatial structural characteristic of surface materials, the performance of the hyperspectral image classification can be boosted by incorporating texture information. Normally, the spatial structure can be extracted by predefined operators, including the popular extended multiattribute profiles (EMAPs) and the Gabor filters. Recently, superpixel segmentation, which reflects the homogeneous regularity of objects, has drawn much attention in the field. In this paper, a collaborative representation-based multiscale superpixel fusion (CRMSF) approach has been proposed for the hyperspectral image classification. First, after obtaining the EMAPs from the raw hyperspectral image, a group of predesigned 3-D Gabor wavelet filters is convolved with the EMAP features, and the EMAP-Gabor features can, thus, be achieved. Second, the collaborative representation-based classification (CRC) is employed to fully and efficiently make use of the huge amount of extracted EMAP-Gabor features. Third, multiscale superpixel maps are generated from the EMAP features that are utilized to regularize the classification map obtained by CRC. A heuristic strategy has been specially devised to automatically decide the number of extracted superpixels in multiple scales, which can be perfectly compatible with hyperspectral images having various spatial sizes and spatial resolutions. This is the most important contribution of the developed CRMSF approach. Finally, the classification task is accomplished by fusing the multiple regularized classification maps. The CRMSF approach has been evaluated on four popular hyperspectral image data sets, and the experimental results show the advantages of CRMSF, particularly for a hyperspectral image with high spatial resolution.

ACS Style

Sen Jia; Xianglong Deng; Jiasong Zhu; Meng Xu; Jun Zhou; Xiuping Jia. Collaborative Representation-Based Multiscale Superpixel Fusion for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2019, 57, 7770 -7784.

AMA Style

Sen Jia, Xianglong Deng, Jiasong Zhu, Meng Xu, Jun Zhou, Xiuping Jia. Collaborative Representation-Based Multiscale Superpixel Fusion for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2019; 57 (10):7770-7784.

Chicago/Turabian Style

Sen Jia; Xianglong Deng; Jiasong Zhu; Meng Xu; Jun Zhou; Xiuping Jia. 2019. "Collaborative Representation-Based Multiscale Superpixel Fusion for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing 57, no. 10: 7770-7784.

Journal article
Published: 29 April 2019 in IEEE Transactions on Intelligent Transportation Systems
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Although 3-D reconstruction of dynamic road environment by moving cameras has been broadly applied in recognition and navigation systems, this task is still considered challenging, especially under circumstances with moving objects, where the reconstruction precision is strongly harassed by the ghosting problem. To address this issue, in this paper, we propose a novel approach for reconstructing 3-D maps of complete static scenes, based on a combination of an elaborately designed moving-object filtering mechanism and a map repairing and blank refilling procedure, where both plausible color and depth information from stereo image pairs are utilized. In this approach, first, we employ the planarity knowledge into the initial depth map based on the simple linear iterative cluster (SLIC) superpixel segmentation. The dynamic area in the image is determined under the supervision of odometry calculation. After wiping off moving objects, by collaboratively repairing color and depth information, the final 3-D map containing only static scene is obtained. The experimental results on extensive challenging real-world scenarios demonstrate the effectiveness and robustness of our approach.

ACS Style

Jiasong Zhu; Lei Fan; Wei Tian; Long Chen; Dongpu Cao; Fei-Yue Wang. Toward the Ghosting Phenomenon in a Stereo-Based Map With a Collaborative RGB-D Repair. IEEE Transactions on Intelligent Transportation Systems 2019, 1 -11.

AMA Style

Jiasong Zhu, Lei Fan, Wei Tian, Long Chen, Dongpu Cao, Fei-Yue Wang. Toward the Ghosting Phenomenon in a Stereo-Based Map With a Collaborative RGB-D Repair. IEEE Transactions on Intelligent Transportation Systems. 2019; (99):1-11.

Chicago/Turabian Style

Jiasong Zhu; Lei Fan; Wei Tian; Long Chen; Dongpu Cao; Fei-Yue Wang. 2019. "Toward the Ghosting Phenomenon in a Stereo-Based Map With a Collaborative RGB-D Repair." IEEE Transactions on Intelligent Transportation Systems , no. 99: 1-11.

Journal article
Published: 03 January 2019 in Remote Sensing
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This paper presents a novel indoor topological localization method based on mobile phone videos. Conventional methods suffer from indoor dynamic environmental changes and scene ambiguity. The proposed Visual Landmark Sequence-based Indoor Localization (VLSIL) method is capable of addressing problems by taking steady indoor objects as landmarks. Unlike many feature or appearance matching-based localization methods, our method utilizes highly abstracted landmark sematic information to represent locations and thus is invariant to illumination changes, temporal variations, and occlusions. We match consistently detected landmarks against the topological map based on the occurrence order in the videos. The proposed approach contains two components: a convolutional neural network (CNN)-based landmark detector and a topological matching algorithm. The proposed detector is capable of reliably and accurately detecting landmarks. The other part is the matching algorithm built on the second order hidden Markov model and it can successfully handle the environmental ambiguity by fusing sematic and connectivity information of landmarks. To evaluate the method, we conduct extensive experiments on the real world dataset collected in two indoor environments, and the results show that our deep neural network-based indoor landmark detector accurately detects all landmarks and is expected to be utilized in similar environments without retraining and that VLSIL can effectively localize indoor landmarks.

ACS Style

Jiasong Zhu; Qing Li; Rui Cao; Ke Sun; Tao Liu; Jonathan M. Garibaldi; Qingquan Li; Bozhi Liu; Guoping Qiu. Indoor Topological Localization Using a Visual Landmark Sequence. Remote Sensing 2019, 11, 73 .

AMA Style

Jiasong Zhu, Qing Li, Rui Cao, Ke Sun, Tao Liu, Jonathan M. Garibaldi, Qingquan Li, Bozhi Liu, Guoping Qiu. Indoor Topological Localization Using a Visual Landmark Sequence. Remote Sensing. 2019; 11 (1):73.

Chicago/Turabian Style

Jiasong Zhu; Qing Li; Rui Cao; Ke Sun; Tao Liu; Jonathan M. Garibaldi; Qingquan Li; Bozhi Liu; Guoping Qiu. 2019. "Indoor Topological Localization Using a Visual Landmark Sequence." Remote Sensing 11, no. 1: 73.

Journal article
Published: 15 November 2018 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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This paper presents an advanced urban traffic density estimation solution using the latest deep learning techniques to intelligently process ultrahigh-resolution traffic videos taken from an unmanned aerial vehicle (UAV). We first capture nearly an hour-long ultrahigh-resolution traffic video at five busy road intersections of a modern megacity by flying a UAV during the rush hours. We then randomly sampled over 17 K 512 × 512 pixel image patches from the video frames and manually annotated over 64 K vehicles to form a dataset for this paper, which will also be made available to the research community for research purposes. Our innovative urban traffics analysis solution consists of an advanced deep neural network (DNN) based vehicle detection and localization, type (car, bus, and truck) recognition, tracking, and vehicle counting over time. We will present extensive experimental results to demonstrate the effectiveness of our solution. We will show that our enhanced single shot multibox detector (Enhanced-SSD) outperforms other DNN-based techniques and that deep learning techniques are more effective than traditional computer vision techniques in traffic video analysis. We will also show that ultrahigh-resolution video provides more information that enables more accurate vehicle detection and recognition than lower resolution contents. This paper not only demonstrates the advantages of using the latest technological advancements (ultrahigh-resolution video and UAV), but also provides an advanced DNN-based solution for exploiting these technological advancements for urban traffic density estimation.

ACS Style

Jiasong Zhu; Ke Sun; Sen Jia; Qingquan Li; Xianxu Hou; Weidong Lin; Bozhi Liu; Guoping Qiu. Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2018, 11, 4968 -4981.

AMA Style

Jiasong Zhu, Ke Sun, Sen Jia, Qingquan Li, Xianxu Hou, Weidong Lin, Bozhi Liu, Guoping Qiu. Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018; 11 (12):4968-4981.

Chicago/Turabian Style

Jiasong Zhu; Ke Sun; Sen Jia; Qingquan Li; Xianxu Hou; Weidong Lin; Bozhi Liu; Guoping Qiu. 2018. "Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 12: 4968-4981.

Journal article
Published: 27 September 2018 in Remote Sensing
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Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban land use at pixel level using high-resolution aerial images and ground-level street view images. We use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fused together through a deep neural network for classifying land use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high classification accuracy, the ground-level street view images contain useful information for urban land use classification, and fusing street image features with aerial images can improve classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide useful auxiliary information to aerial images to boost performances.

ACS Style

Rui Cao; Jiasong Zhu; Wei Tu; Qingquan Li; Jinzhou Cao; Bozhi Liu; Qian Zhang; Guoping Qiu. Integrating Aerial and Street View Images for Urban Land Use Classification. Remote Sensing 2018, 10, 1553 .

AMA Style

Rui Cao, Jiasong Zhu, Wei Tu, Qingquan Li, Jinzhou Cao, Bozhi Liu, Qian Zhang, Guoping Qiu. Integrating Aerial and Street View Images for Urban Land Use Classification. Remote Sensing. 2018; 10 (10):1553.

Chicago/Turabian Style

Rui Cao; Jiasong Zhu; Wei Tu; Qingquan Li; Jinzhou Cao; Bozhi Liu; Qian Zhang; Guoping Qiu. 2018. "Integrating Aerial and Street View Images for Urban Land Use Classification." Remote Sensing 10, no. 10: 1553.

Journal article
Published: 06 June 2018 in Remote Sensing
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Vehicle behavior recognition is an attractive research field which is useful for many computer vision and intelligent traffic analysis tasks. This paper presents an all-in-one behavior recognition framework for moving vehicles based on the latest deep learning techniques. Unlike traditional traffic analysis methods which rely on low-resolution videos captured by road cameras, we capture 4K ( 3840×2178 ) traffic videos at a busy road intersection of a modern megacity by flying a unmanned aerial vehicle (UAV) during the rush hours. We then manually annotate locations and types of road vehicles. The proposed method consists of the following three steps: (1) vehicle detection and type recognition based on deep neural networks; (2) vehicle tracking by data association and vehicle trajectory modeling; (3) vehicle behavior recognition by nearest neighbor search and by bidirectional long short-term memory network, respectively. This paper also presents experimental results of the proposed framework in comparison with state-of-the-art approaches on the 4K testing traffic video, which demonstrated the effectiveness and superiority of the proposed method.

ACS Style

Jiasong Zhu; Ke Sun; Sen Jia; Weidong Lin; Xianxu Hou; Bozhi Liu; Guoping Qiu. Bidirectional Long Short-Term Memory Network for Vehicle Behavior Recognition. Remote Sensing 2018, 10, 887 .

AMA Style

Jiasong Zhu, Ke Sun, Sen Jia, Weidong Lin, Xianxu Hou, Bozhi Liu, Guoping Qiu. Bidirectional Long Short-Term Memory Network for Vehicle Behavior Recognition. Remote Sensing. 2018; 10 (6):887.

Chicago/Turabian Style

Jiasong Zhu; Ke Sun; Sen Jia; Weidong Lin; Xianxu Hou; Bozhi Liu; Guoping Qiu. 2018. "Bidirectional Long Short-Term Memory Network for Vehicle Behavior Recognition." Remote Sensing 10, no. 6: 887.

Journal article
Published: 01 May 2018 in IEICE Transactions on Information and Systems
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ACS Style

Xianxu Hou; Jiasong Zhu; Ke Sun; Linlin Shen; Guoping Qiu. Object Specific Deep Feature for Face Detection. IEICE Transactions on Information and Systems 2018, E101.D, 1270 -1277.

AMA Style

Xianxu Hou, Jiasong Zhu, Ke Sun, Linlin Shen, Guoping Qiu. Object Specific Deep Feature for Face Detection. IEICE Transactions on Information and Systems. 2018; E101.D (5):1270-1277.

Chicago/Turabian Style

Xianxu Hou; Jiasong Zhu; Ke Sun; Linlin Shen; Guoping Qiu. 2018. "Object Specific Deep Feature for Face Detection." IEICE Transactions on Information and Systems E101.D, no. 5: 1270-1277.

Journal article
Published: 22 March 2018 in Resources, Conservation and Recycling
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The urgent need to develop low carbon urban transport systems particularly in Asian megacities is facing the significant challenge of growing motorization following population increase and economic development. Sustainable urban public transport (UPT) plays a crucial role to fulfil the ambitious targets on carbon emission reduction. In this study, life cycle assessment was employed to quantify the environmental impacts (measured by carbon emissions) of UPT systems (including bus and subway) in Shenzhen, a leading megacity in South China, and then to examine corresponding carbon intensity reduction potentials. Results showed that the total carbon emissions from UPT in Shenzhen have increased rapidly from 0.70 Mt in 2005 to 1.74 Mt in 2015 due to the fast growth of the volume of transport turnover. However, current low-carbon UPT mode has only reduced 0.21 Mt CO2 e (cumulative value, from 2005 to 2015), and thus could not contribute proportionally to the city’s overall emission reduction target. Three advanced scenarios (from conservative to optimistic) were further simulated to estimate carbon emissions and their intensity reduction potentials over the next 15 years (2016–2030). Compared to the business-as-usual scenario, all these three low-carbon transition scenarios could significantly mitigate the rapid growth of carbon emissions and consequently help achieve Shenzhen’s carbon intensity reduction goal by 2030 (60%, compared to 2005 level). These findings could not only inform evidence-based policy making to facilitate the low-carbon transition of the urban transport sector in Shenzhen, but also shed light on sustainable urban transition in other megacities.

ACS Style

Dan Dong; Huabo Duan; Ruichang Mao; Qingbin Song; Jian Zuo; Jiasong Zhu; Gang Wang; Mingwei Hu; Biqin Dong; Gang Liu. Towards a low carbon transition of urban public transport in megacities: A case study of Shenzhen, China. Resources, Conservation and Recycling 2018, 134, 149 -155.

AMA Style

Dan Dong, Huabo Duan, Ruichang Mao, Qingbin Song, Jian Zuo, Jiasong Zhu, Gang Wang, Mingwei Hu, Biqin Dong, Gang Liu. Towards a low carbon transition of urban public transport in megacities: A case study of Shenzhen, China. Resources, Conservation and Recycling. 2018; 134 ():149-155.

Chicago/Turabian Style

Dan Dong; Huabo Duan; Ruichang Mao; Qingbin Song; Jian Zuo; Jiasong Zhu; Gang Wang; Mingwei Hu; Biqin Dong; Gang Liu. 2018. "Towards a low carbon transition of urban public transport in megacities: A case study of Shenzhen, China." Resources, Conservation and Recycling 134, no. : 149-155.

Journal article
Published: 11 February 2018 in Sensors
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In this paper, an improved method based on a mixture of Gaussian and quadrilateral functions is presented to process airborne bathymetric LiDAR waveforms. In the presented method, the LiDAR waveform is fitted to a combination of three functions: one Gaussian function for the water surface contribution, another Gaussian function for the water bottom contribution, and a new quadrilateral function to fit the water column contribution. The proposed method was tested on a simulated dataset and a real dataset, with the focus being mainly on the performance of retrieving bottom response and water depths. We also investigated the influence of the parameter settings on the accuracy of the bathymetry estimates. The results demonstrate that the improved quadrilateral fitting algorithm shows a superior performance in terms of low RMSE and a high detection rate in the water depth and magnitude retrieval. What’s more, compared with the use of a triangular function or the existing quadrilateral function to fit the water column contribution, the presented method retrieved the least noise and the least number of unidentified waveforms, showed the best performance in fitting the return waveforms, and had consistent fitting goodness for all different water depths.

ACS Style

Kai Ding; Qingquan Li; Jiasong Zhu; Chisheng Wang; Minglei Guan; Zhipeng Chen; Chao Yang; Yang Cui; Jianghai Liao. An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms. Sensors 2018, 18, 552 .

AMA Style

Kai Ding, Qingquan Li, Jiasong Zhu, Chisheng Wang, Minglei Guan, Zhipeng Chen, Chao Yang, Yang Cui, Jianghai Liao. An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms. Sensors. 2018; 18 (2):552.

Chicago/Turabian Style

Kai Ding; Qingquan Li; Jiasong Zhu; Chisheng Wang; Minglei Guan; Zhipeng Chen; Chao Yang; Yang Cui; Jianghai Liao. 2018. "An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms." Sensors 18, no. 2: 552.

Journal article
Published: 16 October 2017 in IEEE Transactions on Geoscience and Remote Sensing
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Since it is usually difficult and time-consuming to obtain sufficient training samples by manually labeling, feature extraction, which investigates the characteristics of hyperspectral images (HSIs), such as spectral continuity and spatial locality of surface objects, to achieve the most discriminative feature representation, is very important for HSI classification. Meanwhile, due to the spatial regularity of surface materials, it is desirable to improve the classification performance of HSIs from the superpixel viewpoint. In this paper, we propose a novel local binary pattern (LBP)-based superpixel-level decision fusion method for HSI classification. The proposed framework employs uniform LBP (ULBP) to extract local image features, and then, a support vector machine is utilized to formulate the probability description of each pixel belonging to every class. The composite image of the first three components extracted by a principal component analysis from the HSI data is oversegmented into many homogeneous regions by using the entropy rate segmentation method. Then, a region merging process is applied to make the superpixels obtained more homogeneous and agree with the spatial structure of materials more precisely. Finally, a probability-oriented classification strategy is applied to classify each pixel based on superpixel-level guidance. The proposed framework "ULBP-based superpixel-level decision fusion framework" is named ULBP-SPG. Experimental results on two real HSI data sets have demonstrated that the proposed ULBP-SPG framework is more effective and powerful than several state-of-the-art methods.

ACS Style

Sen Jia; Bin Deng; Jiasong Zhu; Xiuping Jia; Qingquan Li. Local Binary Pattern-Based Hyperspectral Image Classification With Superpixel Guidance. IEEE Transactions on Geoscience and Remote Sensing 2017, 56, 749 -759.

AMA Style

Sen Jia, Bin Deng, Jiasong Zhu, Xiuping Jia, Qingquan Li. Local Binary Pattern-Based Hyperspectral Image Classification With Superpixel Guidance. IEEE Transactions on Geoscience and Remote Sensing. 2017; 56 (2):749-759.

Chicago/Turabian Style

Sen Jia; Bin Deng; Jiasong Zhu; Xiuping Jia; Qingquan Li. 2017. "Local Binary Pattern-Based Hyperspectral Image Classification With Superpixel Guidance." IEEE Transactions on Geoscience and Remote Sensing 56, no. 2: 749-759.

Journal article
Published: 06 August 2017 in ISPRS International Journal of Geo-Information
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The spatial interpolation of property fields in 3D, such as the temperature, salinity, and organic content of ocean water, is an active area of research in the applied geosciences. Conventional interpolation methods have not adequately addressed anisotropy in these data. Thus, in our research we considered two interpolation methods based on a triangular prism volume element, as a triangular prism structure best represents directivity, to express the anisotropy inherent in geological property fields. A linear triangular prism interpolation is proposed for layered stratum that achieves a complete continuity based on the volume coordinates of the triangular prism. A triangular prism quadric interpolation (a unit function of a triangular prism spline with 15 nodes) is designed for a smooth transition between adjacent triangular prisms with approximately continuity, expressing the continuity of the entire model. We designed a specific model which accounts for the different spatial correlations in three dimensions. We evaluated the accuracy of our proposed linear and triangular prism quadric interpolation methods with traditional inverse distance weighting (IDW) and kriging interpolation approaches in comparative experiments. The results show that, in 3D geological modeling, the linear and quadric triangular prism interpolations more accurately represent the changes in the property values of the layered strata than the IDW and kriging interpolation methods. Furthermore, the triangular prism quadric interpolation algorithm with 15 nodes outperforms the other methods. This study of triangular prism interpolation algorithms has implications for the expression of data fields with 3D properties. Moreover, our novel approach will contribute to spatial attribute prediction and representation and is applicable to all 3D geographic information; for example, in studies of atmospheric circulation, ocean circulation, water temperature, salinity, and three-dimensional pollutant diffusion.

ACS Style

Yang Cui; Qingquan Li; Jiasong Zhu; Chisheng Wang; Kai Ding; Dan Wang; Bisheng Yang. A Triangular Prism Spatial Interpolation Method for Mapping Geological Property Fields. ISPRS International Journal of Geo-Information 2017, 6, 241 .

AMA Style

Yang Cui, Qingquan Li, Jiasong Zhu, Chisheng Wang, Kai Ding, Dan Wang, Bisheng Yang. A Triangular Prism Spatial Interpolation Method for Mapping Geological Property Fields. ISPRS International Journal of Geo-Information. 2017; 6 (8):241.

Chicago/Turabian Style

Yang Cui; Qingquan Li; Jiasong Zhu; Chisheng Wang; Kai Ding; Dan Wang; Bisheng Yang. 2017. "A Triangular Prism Spatial Interpolation Method for Mapping Geological Property Fields." ISPRS International Journal of Geo-Information 6, no. 8: 241.

Journal article
Published: 28 March 2017 in IEEE Transactions on Cybernetics
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As manual labeling is very difficult and time-consuming, the labeled samples used to train a supervised classifier are generally limited, which become one of the biggest challenge for hyperspectral imagery classification. In order to tackle this issue, a recent trend is to exploit the structure information of materials, as which reflects the region homogeneity in the spatial domain and offers an invaluable complement to the spectral information. In this respect, 3-D Gabor wavelets have been introduced to extract joint spectral-spatial features for hyperspectral images. One the one hand, the features extracted by 3-D Gabor wavelets lead to very good performance for classification. On the other hand, its drawbacks, i.e., big number of features and high computational cost, limit its applicability. In this paper, a 3-D Gabor-wavelet-based phase coding and Hamming distance-based matching (3DGPC-HDM) framework is developed for hyperspectral imagery classification. The proposed method, instead of taking into account the large volume of Gabor magnitude features, exploits the Gabor phase features with certain orientations (i.e., the direction parallel to the spectral axis), which are then encoded by a simple quadrant bit coding scheme. After that, a normalized Hamming distance matching (HDM) method is adopted to determine the similarity of two samples, and the nearest neighbor classifier is routinely utilized for pixelwise recognition. Finally, experiments on three real hyperspectral data sets show that the proposed 3DGPC-HDM leads to very good performance. Comparisons with the state-of-the-art methods in the literature, in terms of both classifier complexity and generalization ability from very small training sets, are also included.

ACS Style

Sen Jia; Linlin Shen; Jiasong Zhu; Qingquan Li. A 3-D Gabor Phase-Based Coding and Matching Framework for Hyperspectral Imagery Classification. IEEE Transactions on Cybernetics 2017, 48, 1176 -1188.

AMA Style

Sen Jia, Linlin Shen, Jiasong Zhu, Qingquan Li. A 3-D Gabor Phase-Based Coding and Matching Framework for Hyperspectral Imagery Classification. IEEE Transactions on Cybernetics. 2017; 48 (4):1176-1188.

Chicago/Turabian Style

Sen Jia; Linlin Shen; Jiasong Zhu; Qingquan Li. 2017. "A 3-D Gabor Phase-Based Coding and Matching Framework for Hyperspectral Imagery Classification." IEEE Transactions on Cybernetics 48, no. 4: 1176-1188.

Journal article
Published: 10 March 2017 in Transactions in GIS
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The vehicle routing problem (VRP) is one of the most prominent problems in spatial optimization because of its broad applications in both the public and private sectors. This article presents a novel spatial parallel heuristic approach for solving large-scale VRPs with capacity constraints. A spatial partitioning strategy is devised to divide a region of interest into a set of small spatial cells to allow the use of a parallel local search with a spatial neighbor reduction strategy. An additional local search and perturbation mechanism around the border area of spatial cells is used to improve route segments across spatial cells to overcome the border effect. The results of one man-made VRP benchmark and three real-world super-large-scale VRP instances with tens of thousands of nodes verify that the presented spatial parallel heuristic approach achieves a comparable solution with much less computing time.

ACS Style

Wei Tu; Qingquan Li; Jiasong Zhu; Baoding Zhou; Bi Yu Chen. A spatial parallel heuristic approach for solving very large-scale vehicle routing problems. Transactions in GIS 2017, 21, 279 .

AMA Style

Wei Tu, Qingquan Li, Jiasong Zhu, Baoding Zhou, Bi Yu Chen. A spatial parallel heuristic approach for solving very large-scale vehicle routing problems. Transactions in GIS. 2017; 21 (6):279.

Chicago/Turabian Style

Wei Tu; Qingquan Li; Jiasong Zhu; Baoding Zhou; Bi Yu Chen. 2017. "A spatial parallel heuristic approach for solving very large-scale vehicle routing problems." Transactions in GIS 21, no. 6: 279.

Journal article
Published: 03 March 2017 in Scientific Reports
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The time-series topography change of a landfill site before its failure has rarely been surveyed in detail. However, this information is important for both landfill management and early warning of landslides. Here, we take the 2015 Shenzhen landslide as an example, and we use the radar shape-from-shading (SFS) technique to retrieve time-series digital elevation models of the landfill. The results suggest that the total filling volume reached 4,074,300 m3 in the one and a half years before the landslide, while 2,817,400 m3 slid down in the accident. Meanwhile, the landfill rate in most areas exceeded 2 m/month, which is the empirical upper threshold in landfill engineering. Using topography captured on December 12, 2015, the slope safety analysis gives a factor of safety of 0.932, suggesting that this slope was already hazardous before the landslide. We conclude that the synthetic aperture radar (SAR) SFS technique has the potential to contribute to landfill failure monitoring.

ACS Style

Chisheng Wang; Qingquan Li; Jiasong Zhu; Wei Gao; Xinjian Shan; Jun Song; Xiaoli Ding. Formation of the 2015 Shenzhen landslide as observed by SAR shape-from-shading. Scientific Reports 2017, 7, 43351 .

AMA Style

Chisheng Wang, Qingquan Li, Jiasong Zhu, Wei Gao, Xinjian Shan, Jun Song, Xiaoli Ding. Formation of the 2015 Shenzhen landslide as observed by SAR shape-from-shading. Scientific Reports. 2017; 7 (1):43351.

Chicago/Turabian Style

Chisheng Wang; Qingquan Li; Jiasong Zhu; Wei Gao; Xinjian Shan; Jun Song; Xiaoli Ding. 2017. "Formation of the 2015 Shenzhen landslide as observed by SAR shape-from-shading." Scientific Reports 7, no. 1: 43351.