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Fusion of Super-Resolution and Semantic Segmentation Deep Models for Building Footprint Extraction From Aerial Images

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The Photogrammetric Record 2025年第190期40卷

作者： Ziyi Chen Yuhua Luo Jiaying Zhang Ronggang Guo Liai Deng Jinghua Liu Dilong Li Yongtao Yu Ammar Abulibdeh Cheng Wang Department of Computer Science and Technology Fujian Key Laboratory of big Data Intelligence and Security Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen FJ China Guojiao Spatial Information Technology (Beijing) co. Ltd Beijing China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian JS China Department of Humanities College of Arts and Sciences Qatar University Doha Qatar School of Informatics Xiamen University Xiamen FJ China

The spatial resolution of remotely sensed images has seen significant improvements; higher resolution facilitates the understanding of remote sensing images and improves the accuracy of building footprint extraction. Thus, this paper introduces a novel approach that combines super-resolution reconstruction with semantic segmentation deep models for building footprint extraction. To begin, a super-resolution reconstruction deep model is employed to enhance the resolution of the target images. Subsequently, training samples are generated using various data augmentation techniques based on the reconstructed images. Finally, a semantic segmentation deep model is trained to perform the final building footprint extraction. For validation, our proposed framework was tested on two publicly available aerial image datasets: the WHU building dataset and the Massachusetts building dataset. The results obtained from these datasets demonstrated the effectiveness of our method, achieving high Intersection over Union (IoU) scores of 90.57% and 75.02%, respectively. Furthermore, our approach outperformed seven other competing methods, showcasing its superior performance.

关键词： aerial image building footprint extraction deep learning semantic segmentation super-resolution

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The Development of Proxy Re-Encryption

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Journal of Cyber Security 2020年第1期2卷 1-8页

作者： Yepeng Liu Yongjun Ren Qirun Wang Jinyue Xia School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjingChina School of Engineering and Technology University of HertfordshireHertfordUK International Business Machines Corporation(IBM) New YorkUSA

With the diversification of electronic devices,cloud-based services have become the link between different *** a cryptosystem with secure conversion function,proxy re-encryption enables secure sharing of data in a cloud *** re-encryption is a public key encryption system with ciphertext security conversion function.A semi-trusted agent plays the role of ciphertext conversion,which can convert the user ciphertext into the same plaintext encrypted by the principal’s public *** re-encryption has been a hotspot in the field of information security since it was proposed by Blaze et al.[Blaze,Bleumer and Strauss(1998)].After 20 years of development,proxy re-encryption has evolved into many forms been widely *** paper elaborates on the definition,characteristics and development status of proxy re-encryption,and classifies proxy re-encryption from the perspectives of user identity,conversion condition,conversion hop count and conversion *** aspects of the existing program were compared and briefly reviewed from the aspects of features,performance,and ***,this paper looks forward to the possible development direction of proxy re-encryption in the future.

关键词： Proxy re-encryption bilinear pairing information security

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APPLE-MASNUM: Accelerating parallel processing for lightweight expansion of MASNUM on a single multi-GPU node

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Ocean Modelling 2025年 196卷

作者： Lou, Qi Wu, Changmao Dong, Changming Feng, Xingru Xia, Yuanyuan Liu, Li Xu, Zhengwei Gao, Xu Sun, Meng Yin, Xunqiang Institute of Software Chinese Academy of Sciences Beijing China School of Marine sciences Nanjing University of Information Science and Technology Nanjing China CAS Key Laboratory of Ocean Circulation and Waves Institute of Oceanology Chinese Academy of Sciences Qingdao China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China College of Computer and Information Engineering Henan Normal University Xinxiang China National Supercomputing Center in Zhengzhou Zhengzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China First Institute of Oceanography Ministry of Natural Resources Qingdao China Ministry of Natural Resources Qingdao China

The Marine science and Numerical Modeling (MASNUM) system, developed for oceanic wave forecasting, play an important role in marine disaster prevention and maritime activities. However, its application is hampered by the requirement of large computing resources. To overcome these barriers, we have implemented an accelerating parallel processing for lightweight expansion of MASNUM (APPLE-MASNUM) on a single compute node with multiple GPUs. In initiating our approach, the mathematical-physics equations of the MASNUM system are thoroughly analyzed to pinpoint the primary computational bottlenecks. This study then transforms MASNUM from a multi-process MPI program into a preliminary GPU-compatible algorithms. Subsequently, the paper proposes an optimization strategy for two-dimensional four-point stencil computations. Following this, an optimization method for overlapping computation with communication is introduced. Finally, a refined data layout scheme tailored for GPUs is designed and implemented. Three numerical experiments with five-day wave forecasts demonstrated that compared to single-core MASNUM, the acceleration ratios of the framework presented in this study are 49.29-fold, 62.58-fold, and 65.74-fold, respectively. This considerable performance boost highlights the efficiency of the lightweight APPLE-MASNUM framework introduced in this research. This signifies the first implementation and optimization of the MASNUM model on a GPU-based heterogeneous platform. © 2025 Elsevier Ltd

关键词： Digital arithmetic

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A novel controllable image segmentation method using the inverse GAMMA distribution function based on histogram specification

A novel controllable image segmentation method using the inv...

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2019 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2019

作者： Saeed, Dhekra Shi, Huibin Ameen, Barakat College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China College of Information Science and Engineering Central South University Changsha China

ISBN: (纸本)9781728161068

Image segmentation(IMSEG) is defined as a top of the basic and most significant process of digital image handling which refers to the techniques that used to partitioning and dividing an image into useful and meaningful parts, called segments. It's so important for many applications which difficult and inefficient to process the entire image, such as object recognition or image compression. So, IMSEG aims to segment the image into districts for further operations and processing. There are several techniques for image segmentation which divide the image into many parts regarding to some specific image properties such as the intensity value of pixels, relation between pixels, color, texture and so on. However, most image segmentation techniques have deficient ability to plainly tune the segment sensitivity of the segmented image. In our manuscript we will propose a new technique for image segmentations using the invers GAMMA distribution function based on histogram matching, the proposed technique provides a method to manage the segment sensitivity of segmented image via setting one parameter. Experimental outcomes pretend a superior execution of this created technique. © 2019 IEEE.

关键词： Image segmentation

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Context aware 3d unet for brain tumor segmentation

arXiv

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arXiv 2020年

作者： Ahmad, Parvez Qamar, Saqib Shen, Linlin Saeed, Adnan National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China School of Hydropower and Information Technology Huazhong University of Science and Technology Wuhan430074 China

Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contexual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose a modified UNet architecture for brain tumor segmentation. In the proposed architecture, we used densely connected blocks in both encoder and decoder paths to extract multi-contexual information from the concept of feature reusability. The proposed residual inception blocks (RIB) are used to extract local and global information by merging features of different kernel sizes. We validate the proposed architecture on the multimodal brain tumor segmentation challenges (BRATS) 2020 testing dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancement tumor (ET) are 89.12%, 84.74%, and 79.12%, respectively. Our proposed work is in the top ten methods based on the dice scores of the testing dataset. © 2020, CC-BY.

关键词： Tumors

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Blockchain-based approach for securing spectrum trading in multibeam satellite systems

arXiv

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arXiv 2020年

作者： Li, Feng Lam, Kwok-Yan Jia, Min Zhao, Jun Li, Xiuhua Wang, Li School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China School of Big Data & Software Engineering Chongqing University Chongqing401331 China College of Marine Electrical Engineering Dalian Maritime University Dalian116026 China

This paper presents a blockchain-based approach for securing spectrum sharing in multi-beam satellite systems. Satellite spectrum is a scarce resource that requires highly efficient management schemes for optimized sharing by network users. However, spectrum sharing is vulnerable to attacks by malicious protocol participants. In order to ensure efficient spectrum management in the face of dishonest satellite users or cyber attackers, it is important for spectrum sharing mechanism to provide transparency and traceability of the trading process so as to enable the system to detect, and hence eliminate, unauthorized access by malicious users. We address these requirements by proposing the use of blockchain which, apart from its ability to provide transparency and traceability, ensures an immutable means for keeping track of user trading reputation. Besides, in order to address the practical constraints of heterogeneous user nodes, we also propose the use of edge computing to support users with limited computing power. In this paper, we propose a blockchain-based spectrum trading framework and, based on which, a multibeam satellite spectrum sharing algorithm for interference pricing and heterogeneous spectrum demands is devised to improve the efficiency of satellite spectrum. By leveraging on the system characteristics of blockchain, a dynamic spectrum sharing mechanism with traceability, openness and transparency for whole trading process is presented. Numerical results are also provided to evaluate the system benefits and spectrum pricing of the proposed mechanism. © 2020, CC BY.

关键词： Blockchain

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A grouping approach based on non-uniform binary grid partitioning for crowd evacuation simulation

A grouping approach based on non-uniform binary grid partiti...

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作者： Liu, Hong Li, Yan Li, Wenhao Lu, Dianjie Zhang, Guijuan School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China University of South Australia Adelaide Australia

Small social groups based on kinship or friendships are ubiquitous in human crowds. Therefore, the effect of social groups on crowd evacuations and that of crowd evacuations on social groups must be investigated. To simulate the group phenomenon when an emergency occurs, we propose an improved social force model that takes into account the social group relationship among the population, and based on our proposed model, a novel grouping algorithm predicated on non-uniform binary grid partitioning is put forward. The approach initially maps the individuals into the plane space, and then it adopts top-down binary grid partitioning iteratively until the divided grid contains only the individuals with relations;then, the values of the relation and density of the non-empty grid cells are calculated, and the grids are sorted according to these values. After sorting, selecting, merging, and forming the core grids, the other grids are merged to the core grids. We have compared the algorithm with the hierarchical classification algorithm and the grid-based algorithm. The results show that the accuracy, speed, and scalability are all advantages. We also establish a simulation platform to illustrate the proposed grouping algorithm and the improved social force model for crowd evacuation simulation. © 2018 John Wiley & Sons, Ltd.

关键词： Simulation platform

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Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing

arXiv

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arXiv 2021年

作者： Kong, Zhe Zhang, Wentian Liu, Feng Luo, Wenhan Liu, Haozhe Shen, Linlin Ramachandra, Raghavendra College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Norwegian University of Science and Technology Gjøvik2818 Norway

Biometric systems are vulnerable to Presentation Attacks (PA) performed using various Presentation Attack Instruments (PAIs). Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem. In this work, we empirically prove that the initialization of the PAD model is a crucial factor for the generalization, which is rarely discussed in the community. Based on such observation, we proposed a self-supervised learning-based method, denoted as DF-DM. Specifically, DF-DM is based on a global-local view coupled with De-Folding and De-Mixing to derive the task-specific representation for PAD. During De-Folding, the proposed technique will learn region-specific features to represent samples in a local pattern by explicitly minimizing generative loss. While De-Mixing drives detectors to obtain the instance-specific features with global information for more comprehensive representation by minimizing interpolation-based consistency. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both face and fingerprint PAD in more complicated and hybrid datasets when compared with state-of-the-art methods. When training in CASIA-FASD and Idiap Replay-Attack, the proposed method can achieve an 18.60% Equal Error Rate (EER) in OULU-NPU and MSU-MFSD, exceeding baseline performance by 9.54%. The source code of the proposed technique is available at https://***/kongzhecn/dfdm. Copyright © 2021, The Authors. All rights reserved.

关键词： Supervised learning

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Architecture information communication in two OSS projects: The why, who, when, and what

arXiv

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arXiv 2021年

作者： Bi, Tingting Ding, Wei Liang, Peng Tang, Antony School of Computer Science Wuhan University Wuhan430072 China Faculty of Information and Technology Monash University MelbourneVIC3166 Australia Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Software and Services Research Group Vrije Universiteit Amsterdam Amsterdam1101 Netherlands Key Laboratory of Earthquake Geodesy Institute of Seismology China Earthquake Administration China

Architecture information is vital for Open Source software (OSS) development, and mailing list is one of the widely used channels for developers to share and communicate architecture information. This work investigates the nature of architecture information communication (i.e., why, who, when, and what) by OSS developers via developer mailing lists. We employed a multiple case study approach to extract and analyze the architecture information communication from the developer mailing lists of two OSS projects, ArgoUML and Hibernate, during their development life-cycle of over 18 years. Our main findings are: (a) architecture negotiation and interpretation are the two main reasons (i.e., why) of architecture communication;(b) the amount of architecture information communicated in developer mailing lists decreases after the first stable release (i.e., when);(c) architecture communications centered around a few core developers (i.e., who);(d) and the most frequently communicated architecture elements (i.e., what) are Architecture Rationale and Architecture Model. There are a few similarities of architecture communication between the two OSS projects. Such similarities point to how OSS developers naturally gravitate towards the four aspects of architecture communication in OSS development. © 2021, CC BY.

关键词： Open source software

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RF-WTI: Wood Types Identification based on Commodity RFID Devices

RF-WTI: Wood Types Identification based on Commodity RFID De...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Xiangmao Chang Xianghui Zhang Muhammad Waqas Isa Weiwei Wu Yan Li Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Computer Science and Engeneering Southeast University Nanjing China Information and Engineering Nanjing University of Finance and Economics Nanjing China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

The identification of wood is an important problem both in industrial manufacturing and in people's daily life. Traditional methods based on experts are laborious. New technologies based on computer visions rely on high-quality cross section images. In this paper, we take the first attempt to identify the wood type based on commodity RFID devices. A system named RF-WTI is proposed. The main idea of RF-WTI is that different wood types result in different signal changes when RF signals pass through the wood. Specifically, after collecting the changes of Received Signal Strength (RSS) and phase when RFID signals pass through the wood, a feature that is unique for the wood is derived. Then RF-WTI applies a Bayesian neural network to identify wood types. Experimental results show that RF-WTI achieves 92.33% average accuracy for identifying 12 different types of wood.

关键词： RF signals Neural networks Bayes methods Sensors Manufacturing Object recognition Radiofrequency identification

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