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With the deployment of 5G, researchers and experts begin to look forward to 6G. They predict that 6G will be the key driving force for information interaction and social life after 2030. With the help of artificial intelligence (AI), 6G will be a highly autonomous closed-loop network, and will make up for 5G's shortcomings in communications, computing and global coverage, achieving “AI of things (AIoT)”. In 6G life, vehicles may become another indispensable devices for people besides smartphones, and non-polluting, highly safe as well as full-autonomous vehicles will be the goal of vehicular development. In order to ensure the safe driving of future vehicles and meet the entertainment needs of passengers, it is necessary to investigate future 6G vehicular intelligence. In this paper, we will discuss its networking, communications, computing and intelligence, look into future technological developments and applications, and identify forthcoming challenges and research directions.
Hongzhi Guo; Xiaoyi Zhou; Jiajia Liu; Yanning Zhang. Vehicular intelligence in 6G: Networking, communications, and computing. Vehicular Communications 2021, 100399 .
AMA StyleHongzhi Guo, Xiaoyi Zhou, Jiajia Liu, Yanning Zhang. Vehicular intelligence in 6G: Networking, communications, and computing. Vehicular Communications. 2021; ():100399.
Chicago/Turabian StyleHongzhi Guo; Xiaoyi Zhou; Jiajia Liu; Yanning Zhang. 2021. "Vehicular intelligence in 6G: Networking, communications, and computing." Vehicular Communications , no. : 100399.
The fusion of hyperspectral image (HSI) and multispectral image (MSI) refers to enhance the spatial resolution of HSI with the help of a corresponding MSI that has a high spatial resolution to finally obtain an HSI with high resolution in both spatial and spectral domains. In this letter, we propose a variational tensor subspace decomposition-based fusion method to fully explore the differences and correlations among three modes of the HSI tensor. Experimental results on two HSI datasets show that the proposed method can achieve superior performance compared with existing state-of-the-art fusion methods with high computational efficiency.
Yinghui Xing; Yan Zhang; Shuyuan Yang; Yanning Zhang. Hyperspectral and Multispectral Image Fusion via Variational Tensor Subspace Decomposition. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleYinghui Xing, Yan Zhang, Shuyuan Yang, Yanning Zhang. Hyperspectral and Multispectral Image Fusion via Variational Tensor Subspace Decomposition. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleYinghui Xing; Yan Zhang; Shuyuan Yang; Yanning Zhang. 2021. "Hyperspectral and Multispectral Image Fusion via Variational Tensor Subspace Decomposition." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
Person-dependent facial expression recognition has received considerable research attention in recent years. Unfortunately, different identities can adversely influence recognition accuracy, and the recognition task becomes challenging. Other adverse factors, including limited training data and improper measures of facial expressions, can further contribute to the above dilemma. To solve these problems, a novel identity-aware method is proposed in this study. Furthermore, this study also represents the first attempt to fulfill the challenging person-dependent facial expression recognition task based on deep metric learning and facial image synthesis techniques. Technically, a StarGAN is incorporated to synthesize facial images depicting different but complete basic emotions for each identity to augment the training data. Then, a deep-convolutional-neural-network-based network is employed to automatically extract latent features from both real facial images and all synthesized facial images. Next, a Mahalanobis metric network trained based on extracted latent features outputs a learned metric that measures facial expression differences between images, and the recognition task can thus be realized. Extensive experiments based on several well-known publicly available datasets are carried out in this study for performance evaluations. Person-dependent datasets, including CK+, Oulu (all 6 subdatasets), MMI, ISAFE, ISED, etc., are all incorporated. After comparing the new method with several popular or state-of-the-art facial expression recognition methods, its superiority in person-dependent facial expression recognition can be proposed from a statistical point of view.
Wei Huang; Siyuan Zhang; Peng Zhang; Yufei Zha; Yuming Fang; Yanning Zhang. Identity-aware Facial Expression Recognition via Deep Metric Learning based on Synthesized Images. IEEE Transactions on Multimedia 2021, PP, 1 -1.
AMA StyleWei Huang, Siyuan Zhang, Peng Zhang, Yufei Zha, Yuming Fang, Yanning Zhang. Identity-aware Facial Expression Recognition via Deep Metric Learning based on Synthesized Images. IEEE Transactions on Multimedia. 2021; PP (99):1-1.
Chicago/Turabian StyleWei Huang; Siyuan Zhang; Peng Zhang; Yufei Zha; Yuming Fang; Yanning Zhang. 2021. "Identity-aware Facial Expression Recognition via Deep Metric Learning based on Synthesized Images." IEEE Transactions on Multimedia PP, no. 99: 1-1.
Small unmanned aerial vehicles (UAVs) have developed rapidly and are widely used for disaster relief, traffic monitoring and military surveillance. To perform these tasks better, it is necessary to improve the environmental perception ability of UAVs in a dynamic environment, including their static and dynamic perception ability. Specifically, both three-dimensional reconstruction for a static scene and localization for moving objects are required. Simultaneous Localization And Mapping technology has made great progress in static scene structure reconstruction and UAV self-motion estimation. However, accurate real-time localization of moving objects is still challenging. In this article, a global averaging based localization method is proposed to locate moving objects for a small UAV platform. Inspired by global structure from motion, this idea is applied to the localization of moving objects. To solve moving object localization, the relative motion estimation and global position optimisation methods are proposed. The proposed method was tested in various scenarios with a several trajectories. The extensive experimental results demonstrate the robustness and effectiveness of the proposed method.
Xiuchuan Xie; Tao Yang; Yanning Zhang; Bang Liang; Linfeng Liu. Accurate localization of moving objects in dynamic environment for small unmanned aerial vehicle platform using global averaging. IET Computer Vision 2021, 1 .
AMA StyleXiuchuan Xie, Tao Yang, Yanning Zhang, Bang Liang, Linfeng Liu. Accurate localization of moving objects in dynamic environment for small unmanned aerial vehicle platform using global averaging. IET Computer Vision. 2021; ():1.
Chicago/Turabian StyleXiuchuan Xie; Tao Yang; Yanning Zhang; Bang Liang; Linfeng Liu. 2021. "Accurate localization of moving objects in dynamic environment for small unmanned aerial vehicle platform using global averaging." IET Computer Vision , no. : 1.
Existing image fusion methods pay little research attention to human visual characteristics. However, human visual characteristics play an important role in visual processing tasks. To solve this problem, we propose a cross-modal image fusion method that combines illuminance factors and attention mechanisms. Human visual characteristics are studied and simulated in cross-modal image fusion task. Firstly, in order to reject high and low-frequency mixing and reduce the halo effect, we perform cross-modal image multi-scale decomposition. Secondly, in order to remove highlights, the visual saliency map and the deep feature map are combined with the illuminance fusion factor to perform high-low frequency non-linear fusion. Thirdly, the feature maps are selected through a channel attention network to obtain the final fusion map. Finally, we validate our image fusion method on public datasets of infrared and visible images. The experimental results demonstrate the superiority of our fusion method under the complex illumination environment. In addition, the experimental results also demonstrate the effectiveness of our simulation of human visual characteristics.
Aiqing Fang; Xinbo Zhao; Jiaqi Yang; Yanning Zhang; Xiang Zheng. Non-linear and selective fusion of cross-modal images. Pattern Recognition 2021, 119, 108042 .
AMA StyleAiqing Fang, Xinbo Zhao, Jiaqi Yang, Yanning Zhang, Xiang Zheng. Non-linear and selective fusion of cross-modal images. Pattern Recognition. 2021; 119 ():108042.
Chicago/Turabian StyleAiqing Fang; Xinbo Zhao; Jiaqi Yang; Yanning Zhang; Xiang Zheng. 2021. "Non-linear and selective fusion of cross-modal images." Pattern Recognition 119, no. : 108042.
With the extensive application of robots, such as unmanned aerial vehicle (UAV) in exploring unknown environments, visual odometry (VO) algorithms have played an increasingly important role. The environments are diverse, not always textured, or low-textured with insufficient features, making them challenging for mainstream VO. However, for low-texture environment, due to the structural characteristics of man-made scene, the lines are usually abundant. In this paper, we propose a virtual-real hybrid map based monocular visual odometry algorithm. The core idea is that we reprocess line segment features to generate the virtual intersection matching points, which can be used to build the virtual map. Introducing virtual map can improve the stability of the visual odometry algorithm in low-texture environment. Specifically, we first combine unparallel matched line segments to generate virtual intersection matching points, then, based on the virtual intersection matching points, we triangulate to get a virtual map, combined with the real map built upon the ordinary point features to form a virtual-real hybrid 3D map. Finally, using the hybrid map, the continuous camera pose estimation can be solved. Extensive experimental results have demonstrated the robustness and effectiveness of the proposed method in various low-texture scenes.
Xiuchuan Xie; Tao Yang; Yajia Ning; Fangbing Zhang; Yanning Zhang. A Monocular Visual Odometry Method Based on Virtual-Real Hybrid Map in Low-Texture Outdoor Environment. Sensors 2021, 21, 3394 .
AMA StyleXiuchuan Xie, Tao Yang, Yajia Ning, Fangbing Zhang, Yanning Zhang. A Monocular Visual Odometry Method Based on Virtual-Real Hybrid Map in Low-Texture Outdoor Environment. Sensors. 2021; 21 (10):3394.
Chicago/Turabian StyleXiuchuan Xie; Tao Yang; Yajia Ning; Fangbing Zhang; Yanning Zhang. 2021. "A Monocular Visual Odometry Method Based on Virtual-Real Hybrid Map in Low-Texture Outdoor Environment." Sensors 21, no. 10: 3394.
Rainfall-runoff modeling is a challenging and important nonlinear time series problem in hydrological sciences. Recently, among the data-driven rainfall-runoff models, those ones based on the long short-term memory (LSTM) network show good performance. Furthermore, LSTM-based sequence-to-sequence (LSTM-S2S) models achieve promising performance for multi-step-ahead runoff predictions. In this paper, for multi-day-ahead runoff predictions, we propose a novel data-driven model named LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) rainfall-runoff model, which contains m multiple state vectors for m-step-ahead runoff predictions. It differs from the existing LSTM-S2S rainfall-runoff models using only one state vector and is more appropriate for multi-day-ahead runoff predictions. To show its performance and advantages, we compare it with two LSTM-S2S models by testing them on 673 basins of the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) data set. The results show that our LSTM-MSV-S2S model has better performance in general and thus using multiple state vectors is more appropriate for multi-day-ahead runoff predictions.
Hanlin Yin; Xiuwei Zhang; Fandu Wang; Yanning Zhang; Runliang Xia; Jin Jin. Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model. Journal of Hydrology 2021, 598, 126378 .
AMA StyleHanlin Yin, Xiuwei Zhang, Fandu Wang, Yanning Zhang, Runliang Xia, Jin Jin. Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model. Journal of Hydrology. 2021; 598 ():126378.
Chicago/Turabian StyleHanlin Yin; Xiuwei Zhang; Fandu Wang; Yanning Zhang; Runliang Xia; Jin Jin. 2021. "Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model." Journal of Hydrology 598, no. : 126378.
Hyperspectral image (HSI) contains an abundant spatial structure that can be embedded into feature extraction (FE) or classifier (CL) components for pixelwise classification enhancement. Although some existing works have exploited some simple spatial structures (e.g., local similarity) to enhance either the FE or CL component, few of them consider the latent manifold structure and how to simultaneously embed the manifold structure into both components seamlessly. Thus, their performance is still limited, especially in cases with limited or noisy training samples. To solve both problems with one stone, we present a novel dual-level deep spatial manifold representation (SMR) network for HSI classification, which consists of two kinds of blocks: an SMR-based FE block and an SMR-based CL block. In both blocks, graph convolution is utilized to adaptively model the latent manifold structure lying in each local spatial area. The difference is that the former block condenses the SMR in deep feature space to form the representation for each center pixel, while the later block leverages the SMR to propagate the label information of other pixels within the local area to the center one. To train the network well, we impose an unsupervised information loss on unlabeled samples and a supervised cross-entropy loss on the labeled samples for joint learning, which empowers the network to utilize sufficient samples for SMR learning. Extensive experiments on two benchmark HSI data set demonstrate the efficacy of the proposed method in terms of pixelwise classification, especially in the cases with limited or noisy training samples.
Cong Wang; Lei Zhang; Wei Wei; Yanning Zhang. Toward Effective Hyperspectral Image Classification Using Dual-Level Deep Spatial Manifold Representation. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -14.
AMA StyleCong Wang, Lei Zhang, Wei Wei, Yanning Zhang. Toward Effective Hyperspectral Image Classification Using Dual-Level Deep Spatial Manifold Representation. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-14.
Chicago/Turabian StyleCong Wang; Lei Zhang; Wei Wei; Yanning Zhang. 2021. "Toward Effective Hyperspectral Image Classification Using Dual-Level Deep Spatial Manifold Representation." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.
Accurate change detection in optical aerial images by using deep learning techniques has been attracting lots of research efforts in recent years. Correct change-detection results usually involve both global and local deep learning features. Existing deep learning approaches have achieved good performance on this task. However, under the scenarios of containing multiscale change areas within a bi-temporal image pair, existing methods still have shortcomings in adapting these change areas, such as false detection and limited completeness in detected areas. To deal with these problems, we design a hierarchical dynamic fusion network (HDFNet) to implement the optical aerial image-change detection task. Specifically, we propose a change-detection framework with hierarchical fusion strategy to provide sufficient information encouraging for change detection and introduce dynamic convolution modules to self-adaptively learn from this information. Also, we use a multilevel supervision strategy with multiscale loss functions to supervise the training process. Comprehensive experiments are conducted on two benchmark datasets, LEBEDEV and LEVIR-CD, to verify the effectiveness of the proposed method and the experimental results show that our model achieves state-of-the-art performance.
Yi Zhang; Lei Fu; Ying Li; Yanning Zhang. HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images. Remote Sensing 2021, 13, 1440 .
AMA StyleYi Zhang, Lei Fu, Ying Li, Yanning Zhang. HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images. Remote Sensing. 2021; 13 (8):1440.
Chicago/Turabian StyleYi Zhang; Lei Fu; Ying Li; Yanning Zhang. 2021. "HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images." Remote Sensing 13, no. 8: 1440.
Deep convolutional neural networks (DCCNs) have shown pleasing performance in single image super-resolution (SISR). To deploy them onto real devices with limited storage and computational resources, a promising solution is to binarize the network, i.e. , quantize each float-point weight and activation into 1 bit. However, existing works on binarizing DCNNs still suffer from severe performance degradation in SISR. To mitigate this problem, we argue that the performance degradation mainly comes from no appropriate constraint on the network weights, which causes it difficult to sensitively reverse the binarization results of these weights using the backpropagated gradient during training and thus limits the flexibility of network in respect of fitting extensive training samples. Inspired by this, we present an embarrassingly simple but effective binarization scheme for SISR, which can obviously relieve the performance degeneration resulted from network binarization and is applicable to different DCNN architectures. Specifically, we force each weight to follow a compact uniform prior, with which the weight will be given a very small absolute value close to zero and its binarization result can be straightforwardly reversed even by a small backpropagated gradient. By doing this, the flexibility and the generalization performance of the binarized network can be improved. Moreover, such a prior performs much better when introducing real identity shortcuts into the network. In addition, to avoid falling into bad local minima during training, we employ a pixel-wise curriculum learning strategy to learn the constrained weights in an easy-to-hard manner. Experiments on four SISR benchmark datasets demonstrate the effectiveness of the proposed binarization method in terms of binarizing different SISR network architectures, e.g. , it even achieves performance comparable to the baseline with 5 quantization bits.
Lei Zhang; Zhiqiang Lang; Wei Wei; Yanning Zhang. Embarrassingly Simple Binarization for Deep Single Imagery Super-Resolution Networks. IEEE Transactions on Image Processing 2021, 30, 3934 -3945.
AMA StyleLei Zhang, Zhiqiang Lang, Wei Wei, Yanning Zhang. Embarrassingly Simple Binarization for Deep Single Imagery Super-Resolution Networks. IEEE Transactions on Image Processing. 2021; 30 ():3934-3945.
Chicago/Turabian StyleLei Zhang; Zhiqiang Lang; Wei Wei; Yanning Zhang. 2021. "Embarrassingly Simple Binarization for Deep Single Imagery Super-Resolution Networks." IEEE Transactions on Image Processing 30, no. : 3934-3945.
The deep learning-based method has shown promising competence in image classification. Its success can be attributed to the ability to learn discriminative feature representation given plenty of labeled data. However, in real-hyperspectral image (HSI) classification applications, since pixel labeling is difficult and costly, the labels we can obtain within an HSI are always limited and noisy (i.e., inaccurate), which consequently causes overfitting of the deep learning-based method. To address this problem, we propose a novel unified deep learning network to employ both labeled and unlabeled data for training, with which the unsupervised structure knowledge, e.g., intracluster similarity and intercluster dissimilarity, inherently contained in those unlabeled data can be exploited to boost the conventional supervised classification. Specifically, we first explore the unsupervised structure knowledge in unlabeled data via a clustering method and formulate a supervised clustering task on those data with the obtained cluster labels. Then, we propose a multitask network to jointly address both the conventional classification task and the formulated supervised clustering task. With a shared feature extraction module and a high-level feature fusion module, the unsupervised structure knowledge contained in unlabeled data can be effectively introduced into the classification task, which is beneficial to learn a more discriminative feature representation and, thus, well mitigates the overfitting problem and improves the classification results. Experimental results on three data sets demonstrate the proposed method can effectively label the unlabeled data within an HSI, especially when the training labels are limited and noisy.
Wei Wei; Songzheng Xu; Lei Zhang; Jinyang Zhang; Yanning Zhang. Boosting Hyperspectral Image Classification With Unsupervised Feature Learning. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -15.
AMA StyleWei Wei, Songzheng Xu, Lei Zhang, Jinyang Zhang, Yanning Zhang. Boosting Hyperspectral Image Classification With Unsupervised Feature Learning. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-15.
Chicago/Turabian StyleWei Wei; Songzheng Xu; Lei Zhang; Jinyang Zhang; Yanning Zhang. 2021. "Boosting Hyperspectral Image Classification With Unsupervised Feature Learning." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-15.
Six-degree-of-freedom (6-DOF) pose estimation from feature correspondences remains a popular and robust approach for 3-D registration. However, heavy outliers that existed in the initial correspondence set pose a great challenge to this problem. This article presents a simple yet effective estimator called SAmple Consensus by sampling COmpatibility Triangles in graphs (SAC-COT) for robust 6-DOF pose estimation and 3-D registration. The key novelty is a guided three-point sampling approach. It is based on a novel correspondence sample representation, i.e., COmpatibility Triangle (COT). We first model the correspondence set as a graph with nodes connecting compatible correspondences. Then, by ranking and sampling COTs formed by ternary loops, we show that correct hypotheses can be generated in early iteration stage. Finally, the hypothesis generated by the COT yielding to the maximum consensus is the output of SAC-COT. Extensive experiments on six data sets and extensive comparisons with the state-of-the-art estimators confirm that: 1) SAC-COT can achieve accurate registrations with a few iterations and 2) SAC-COT outperforms all competitors and is ultrarobust when confronted with Gaussian noise, data decimation, holes, clutter, partial overlap, varying scales of input correspondences, and data modality variation.
Jiaqi Yang; Zhiqiang Huang; Siwen Quan; Zhaoshuai Qi; Yanning Zhang. SAC-COT: Sample Consensus by Sampling Compatibility Triangles in Graphs for 3-D Point Cloud Registration. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -15.
AMA StyleJiaqi Yang, Zhiqiang Huang, Siwen Quan, Zhaoshuai Qi, Yanning Zhang. SAC-COT: Sample Consensus by Sampling Compatibility Triangles in Graphs for 3-D Point Cloud Registration. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-15.
Chicago/Turabian StyleJiaqi Yang; Zhiqiang Huang; Siwen Quan; Zhaoshuai Qi; Yanning Zhang. 2021. "SAC-COT: Sample Consensus by Sampling Compatibility Triangles in Graphs for 3-D Point Cloud Registration." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-15.
Onboard real-time object detection in remote sensing images is a crucial but challenging task in this computation-constrained scenario. This task not only requires the algorithm to yield excellent performance but also requests limited time and space complexity of the algorithm. However, previous convolutional neural networks (CNN) based object detectors for remote sensing images suffer from heavy computational cost, which hinders them from being deployed on satellites. Moreover, an onboard detector is desired to detect objects at vastly different scales. To address these issues, we proposed a lightweight one-stage multi-scale feature fusion detector called MSF-SNET for onboard real-time object detection of remote sensing images. Using lightweight SNET as the backbone network reduces the number of parameters and computational complexity. To strengthen the detection performance of small objects, three low-level features are extracted from the three stages of SNET respectively. In the detection part, another three convolutional layers are designed to further extract deep features with rich semantic information for large-scale object detection. To improve detection accuracy, the deep features and low-level features are fused to enhance the feature representation. Extensive experiments and comprehensive evaluations on the openly available NWPU VHR-10 dataset and DIOR dataset are conducted to evaluate the proposed method. Compared with other state-of-art detectors, the proposed detection framework has fewer parameters and calculations, while maintaining consistent accuracy.
Lang Huyan; Yunpeng Bai; Ying Li; Dongmei Jiang; Yanning Zhang; Quan Zhou; Jiayuan Wei; Juanni Liu; Yi Zhang; Tao Cui. A Lightweight Object Detection Framework for Remote Sensing Images. Remote Sensing 2021, 13, 683 .
AMA StyleLang Huyan, Yunpeng Bai, Ying Li, Dongmei Jiang, Yanning Zhang, Quan Zhou, Jiayuan Wei, Juanni Liu, Yi Zhang, Tao Cui. A Lightweight Object Detection Framework for Remote Sensing Images. Remote Sensing. 2021; 13 (4):683.
Chicago/Turabian StyleLang Huyan; Yunpeng Bai; Ying Li; Dongmei Jiang; Yanning Zhang; Quan Zhou; Jiayuan Wei; Juanni Liu; Yi Zhang; Tao Cui. 2021. "A Lightweight Object Detection Framework for Remote Sensing Images." Remote Sensing 13, no. 4: 683.
Accurate ice segmentation is one of the most crucial techniques for intelligent ice monitoring. Compared with ice segmentation, it can provide more information for ice situation analysis, change trend prediction, and so on. Therefore, the study of ice segmentation has important practical significance. In this study, we focused on fine-grained river ice segmentation using unmanned aerial vehicle (UAV) images. This has the following difficulties: (1) The scale of river ice varies greatly in different images and even in the same image; (2) the same kind of river ice differs greatly in color, shape, texture, size, and so on; and (3) the appearances of different kinds of river ice sometimes appear similar due to the complex formation and change procedure. Therefore, to perform this study, the NWPU_YRCC2 dataset was built, in which all UAV images were collected in the Ningxia–Inner Mongolia reach of the Yellow River. Then, a novel semantic segmentation method based on deep convolution neural network, named ICENETv2, is proposed. To achieve multiscale accurate prediction, we design a multilevel features fusion framework, in which multi-scale high-level semantic features and lower-level finer features are effectively fused. Additionally, a dual attention module is adopted to highlight distinguishable characteristics, and a learnable up-sampling strategy is further used to improve the segmentation accuracy of the details. Experiments show that ICENETv2 achieves the state-of-the-art on the NWPU_YRCC2 dataset. Finally, our ICENETv2 is also applied to solve a realistic problem, calculating drift ice cover density, which is one of the most important factors to predict the freeze-up data of the river. The results demonstrate that the performance of ICENETv2 meets the actual application demand.
Xiuwei Zhang; Yang Zhou; Jiaojiao Jin; Yafei Wang; Minhao Fan; Ning Wang; Yanning Zhang. ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images. Remote Sensing 2021, 13, 633 .
AMA StyleXiuwei Zhang, Yang Zhou, Jiaojiao Jin, Yafei Wang, Minhao Fan, Ning Wang, Yanning Zhang. ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images. Remote Sensing. 2021; 13 (4):633.
Chicago/Turabian StyleXiuwei Zhang; Yang Zhou; Jiaojiao Jin; Yafei Wang; Minhao Fan; Ning Wang; Yanning Zhang. 2021. "ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images." Remote Sensing 13, no. 4: 633.
A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world. CT images have been used as a crucial alternative to the time-consuming RT-PCR test. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. We firstly maintain a large and new chest CT image dataset consisting of 21,658 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. The proposed deep CNN can produce impressive performance effectively.
Qingsen Yan; Bo Wang; Dong Gong; Chuan Luo; Wei Zhao; Jianhu Shen; Jingyang Ai; Qinfeng Shi; Yanning Zhang; Shuo Jin; Liang Zhang; Zheng You. COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations. IEEE Transactions on Big Data 2021, 7, 13 -24.
AMA StyleQingsen Yan, Bo Wang, Dong Gong, Chuan Luo, Wei Zhao, Jianhu Shen, Jingyang Ai, Qinfeng Shi, Yanning Zhang, Shuo Jin, Liang Zhang, Zheng You. COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations. IEEE Transactions on Big Data. 2021; 7 (1):13-24.
Chicago/Turabian StyleQingsen Yan; Bo Wang; Dong Gong; Chuan Luo; Wei Zhao; Jianhu Shen; Jingyang Ai; Qinfeng Shi; Yanning Zhang; Shuo Jin; Liang Zhang; Zheng You. 2021. "COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations." IEEE Transactions on Big Data 7, no. 1: 13-24.
In this paper, we propose an intelligent reflecting surface (IRS) empowered secure cooperative transmission strategy for satellite-terrestrial integrated networks. In such networks, an IRS is deployed near the satellite user to reflect the common-spectrum friendly interference from the terrestrial network to help protect the satellite downlink transmission from being eavesdropped. We seek to minimize the target signal-to-interference-plus-noise-ratio (SINR) at the eavesdropper required to guarantee the reliable communication at the terrestrial network user and to limit the maximum tolerable interference at the satellite user. Then, an alternation optimization scheme is developed. Simulation results verify that the proposed IRS enabled cooperative jamming scheme can reduce the target SINR at the eavesdropper significantly, thus achieving a great secrecy gain.
Sai Xu; Jiajia Liu; Yurui Cao; Jingyi Li; Yanning Zhang. Intelligent Reflecting Surface Enabled Secure Cooperative Transmission for Satellite-Terrestrial Integrated Networks. IEEE Transactions on Vehicular Technology 2021, 70, 2007 -2011.
AMA StyleSai Xu, Jiajia Liu, Yurui Cao, Jingyi Li, Yanning Zhang. Intelligent Reflecting Surface Enabled Secure Cooperative Transmission for Satellite-Terrestrial Integrated Networks. IEEE Transactions on Vehicular Technology. 2021; 70 (2):2007-2011.
Chicago/Turabian StyleSai Xu; Jiajia Liu; Yurui Cao; Jingyi Li; Yanning Zhang. 2021. "Intelligent Reflecting Surface Enabled Secure Cooperative Transmission for Satellite-Terrestrial Integrated Networks." IEEE Transactions on Vehicular Technology 70, no. 2: 2007-2011.
Change detection (CD) is one of the most important topics in the field of remote sensing. In this letter, we propose an effective satellite images CD network named DifUnet++. As the presentation of explicit difference is more conducive to extract change features, we design a differential pyramid of two input images as the input of Unet++. Considering the scale diversity of changed regions in remote sensing images, a multiply side-outs fusion strategy is adopted to predict the detection results of different scales. Furthermore, a learning upsampling method is utilized to refine the details of CD. The proposed architecture is evaluated on two public satellite image CD data sets. The experimental results show that our method performs much better than state-of-the-art methods.
Xiuwei Zhang; Yuanzeng Yue; Wenxiang Gao; Shuai Yun; Qian Su; Hanlin Yin; Yanning Zhang. DifUnet++: A Satellite Images Change Detection Network Based on Unet++ and Differential Pyramid. IEEE Geoscience and Remote Sensing Letters 2021, 1 -5.
AMA StyleXiuwei Zhang, Yuanzeng Yue, Wenxiang Gao, Shuai Yun, Qian Su, Hanlin Yin, Yanning Zhang. DifUnet++: A Satellite Images Change Detection Network Based on Unet++ and Differential Pyramid. IEEE Geoscience and Remote Sensing Letters. 2021; (99):1-5.
Chicago/Turabian StyleXiuwei Zhang; Yuanzeng Yue; Wenxiang Gao; Shuai Yun; Qian Su; Hanlin Yin; Yanning Zhang. 2021. "DifUnet++: A Satellite Images Change Detection Network Based on Unet++ and Differential Pyramid." IEEE Geoscience and Remote Sensing Letters , no. 99: 1-5.
Most learning-based super-resolution (SR) methods aim to recover high-resolution (HR) image from a given low-resolution (LR) image via learning on LR-HR image pairs. The SR methods learned on synthetic data do not perform well in real-world, due to the domain gap between the artificially synthesized and real LR images. Some efforts are thus taken to capture real-world image pairs. However, the captured LR-HR image pairs usually suffer from unavoidable misalignment, which hampers the performance of end- to-end learning. Here, focusing on the real-world SR, we ask a different question: since misalignment is unavoidable, can we propose a method that does not need LR-HR image pairing and alignment at all and utilizes real images as they are? Hence we propose a framework to learn SR from an arbitrary set of unpaired LR and HR images and see how far a step can go in such a realistic and “unsupervised” setting. To do so, we firstly train a degradation generation network to generate realistic LR images and, more importantly, to capture their distribution (i.e., learning to zoom out). Instead of assuming the domain gap has been eliminated, we minimize the discrepancy between the generated data and real data while learning a degradation adaptive SR network (i.e., learning to zoom in). The proposed unpaired method achieves state-of- the-art SR results on real-world images, even in the datasets that favour the paired-learning methods more.
Wei Sun; Dong Gong; Qinfeng Shi; Anton Van Den Hengel; Yanning Zhang. Learning to Zoom-In via Learning to Zoom-Out: Real-World Super-Resolution by Generating and Adapting Degradation. IEEE Transactions on Image Processing 2021, 30, 2947 -2962.
AMA StyleWei Sun, Dong Gong, Qinfeng Shi, Anton Van Den Hengel, Yanning Zhang. Learning to Zoom-In via Learning to Zoom-Out: Real-World Super-Resolution by Generating and Adapting Degradation. IEEE Transactions on Image Processing. 2021; 30 ():2947-2962.
Chicago/Turabian StyleWei Sun; Dong Gong; Qinfeng Shi; Anton Van Den Hengel; Yanning Zhang. 2021. "Learning to Zoom-In via Learning to Zoom-Out: Real-World Super-Resolution by Generating and Adapting Degradation." IEEE Transactions on Image Processing 30, no. : 2947-2962.
Recently, both single modality and cross modality near-duplicate image detection tasks have received wide attention in the community of pattern recognition and computer vision. Existing deep neural networks-based methods have achieved remarkable performance in this task. However, most of the methods mainly focus on the learning of each image from the image pair, thus leading to less use of the information between the near duplicate image pairs to some extent. In this paper, to make more use of the correlations between image pairs, we propose a spatial transformer comparing convolutional neural network (CNN) model to compare near-duplicate image pairs. Specifically, we firstly propose a comparing CNN framework, which is equipped with a cross-stream to fully learn the correlation information between image pairs, while considering the features of each image. Furthermore, to deal with the local deformations led by cropping, translation, scaling, and non-rigid transformations, we additionally introduce a spatial transformer comparing CNN model by incorporating a spatial transformer module to the comparing CNN architecture. To demonstrate the effectiveness of the proposed method on both the single-modality and cross-modality (Optical-InfraRed) near-duplicate image pair detection tasks, we conduct extensive experiments on three popular benchmark datasets, namely CaliforniaND (ND means near duplicate), Mir-Flickr Near Duplicate, and TNO Multi-band Image Data Collection. The experimental results show that the proposed method can achieve superior performance compared with many state-of-the-art methods on both tasks.
Yi Zhang; Shizhou Zhang; Ying Li; Yanning Zhang. Single- and Cross-Modality Near Duplicate Image Pairs Detection via Spatial Transformer Comparing CNN. Sensors 2021, 21, 255 .
AMA StyleYi Zhang, Shizhou Zhang, Ying Li, Yanning Zhang. Single- and Cross-Modality Near Duplicate Image Pairs Detection via Spatial Transformer Comparing CNN. Sensors. 2021; 21 (1):255.
Chicago/Turabian StyleYi Zhang; Shizhou Zhang; Ying Li; Yanning Zhang. 2021. "Single- and Cross-Modality Near Duplicate Image Pairs Detection via Spatial Transformer Comparing CNN." Sensors 21, no. 1: 255.
Unsupervised fusion-based hyperspectral imagery (HSI) super-resolution (SR) is an essential task of HSI processing, which aims to reconstruct a high-resolution (HR) HSI using only an observed low-resolution HSI and a conventional HR image. Although a large number of unsupervised HSI SR methods have been proposed, the heuristic handcrafted image priors adopted by the majority of these methods restrict their capacity to capture specific characteristics of the HSI, as well as their ability to generalize to noisy observation images. In this study, we investigate a fusion-based HSI SR framework with the deep image prior, in which the deep neural network (rather than a heuristic handcrafted image prior) is exploited to capture plenty of image statistics. Within this framework, we further propose an unsupervised recurrence-based HSI SR method using pixel-aware refinement, which utilizes the intermediate reconstruction results to self-supervise unsupervised learning. Due to containing the information of the image-specific characteristic, the proposed method achieves better performance, in terms of both accuracy and robustness to noise, compared with the existing methods. Extensive experiments on four HSI data sets demonstrate the effectiveness of the proposed method.
Wei Wei; Jiangtao Nie; Lei Zhang; Yanning Zhang. Unsupervised Recurrent Hyperspectral Imagery Super-Resolution Using Pixel-Aware Refinement. IEEE Transactions on Geoscience and Remote Sensing 2020, PP, 1 -15.
AMA StyleWei Wei, Jiangtao Nie, Lei Zhang, Yanning Zhang. Unsupervised Recurrent Hyperspectral Imagery Super-Resolution Using Pixel-Aware Refinement. IEEE Transactions on Geoscience and Remote Sensing. 2020; PP (99):1-15.
Chicago/Turabian StyleWei Wei; Jiangtao Nie; Lei Zhang; Yanning Zhang. 2020. "Unsupervised Recurrent Hyperspectral Imagery Super-Resolution Using Pixel-Aware Refinement." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-15.