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Ghazali, Osman, and Shahzada Khurram. "Enhanced IPFIX flow monitoring for VXLAN based cloud overlay networks." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (December 1, 2019): 5519. http://dx.doi.org/10.11591/ijece.v9i6.pp5519-5528.
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<span lang="EN-US">The demands for cloud computing services is rapidly growing due to its fast adoption and the migration of workloads from private data centers to cloud data centers. Many companies, small and large, prefer switching their data to the enterprise cloud environment rather than expanding their own data centers. As a result, the network traffic in cloud data centers is increasing rapidly. However, due to the dynamic resource provisioning and high-speed virtualized cloud networks, the traditional flow-monitoring systems is unable to provide detail visibility and information of traffic traversing the cloud overlay network environment. Hence, it does not fulfill the monitoring requirement of cloud overlay traffic. As the growth of cloud network traffic causes difficulties for the service providers and end-users to manage the traffic efficiently, an enhanced IPFIX flow monitoring mechanism for cloud overlay networks was proposed to address this problem. The monitoring mechanism provided detail visibility and information of overlay network traffic that traversed the cloud environment, which is not available in the current network monitoring systems. The experimental results showed that the proposed monitoring system able to capture overlay network traffic and segregated the tenant traffic based on virtual machines as compare to the standard monitoring system.</span>
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Ramadan, Osama R. S., Mohamed Yasin I. Afifi, and Ahmed Yahya. "A Distributed Cloud Architecture Based on General De Bruijn Overlay Network." International Journal of Cloud Applications and Computing 14, no. 1 (February 27, 2024): 1–19. http://dx.doi.org/10.4018/ijcac.339892.
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Distributed cloud systems enable the distribution of computing resources across various geographical locations. While offering benefits like accelerated content delivery, the scalability and coherence maintenance of these systems pose significant challenges. Recent studies reveal shortcomings in existing distributed system schemes to meet modern cloud application demands and maintain coherence among heterogeneous system elements. This paper proposes a service-oriented network architecture for distributed cloud computing networks. Using a De Bruijn network as a software-defined overlay network, the architecture ensures scalability and coherence. Through service-based addressing, requests are issued to designated service address bands, streamlining service discovery. The architecture's evaluation through extensive simulations showcases sustainable scalability and inherent load-balancing properties. The paper concludes with insights into future research directions, emphasizing the extension of the proposed architecture to emerging distributed cloud use cases and decentralized security.
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Wang, Jessie Hui, Jeffrey Cai, Jerry Lu, Kevin Yin, and Jiahai Yang. "Solving multicast problem in cloud networks using overlay routing." Computer Communications 70 (October 2015): 1–14. http://dx.doi.org/10.1016/j.comcom.2015.05.016.
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Wei, Ming, Ming Zhu, Yaoyuan Zhang, Jiaqi Sun, and Jiarong Wang. "Cyclic Global Guiding Network for Point Cloud Completion." Remote Sensing 14, no. 14 (July 9, 2022): 3316. http://dx.doi.org/10.3390/rs14143316.
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The application of 3D scenes has gradually expanded in recent years. A 3D point cloud is unreliable when it is acquired because of the performance of the sensor. Therefore, it causes difficulties in utilization. Point cloud completion can reconstruct and restore sparse and incomplete point clouds to a more realistic shape. We propose a cyclic global guiding network structure and apply it to point cloud completion tasks. While learning the local details of the whole cloud, our network structure can play a guiding role and will not ignore the overall characteristics of the whole cloud. Based on global guidance, we propose a variety of fitting planes and layered folding attention modules to strengthen the local effect. We use the relationship between the point and the plane to increase the compatibility between the network learning and the original sparse point cloud. We use the attention mechanism of the layer overlay to strengthen the local effect between the encode and decode. Therefore, point clouds are more accurate. Our experiments indicate the effectiveness of our method on the ShapeNet, KITTI, and MVP datasets and are superior to other networks.
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Gharib, Mohammed, Marzieh Malekimajd, and Ali Movaghar. "SLoPCloud: An Efficient Solution for Locality Problem in Peer-to-Peer Cloud Systems." Algorithms 11, no. 10 (October 2, 2018): 150. http://dx.doi.org/10.3390/a11100150.
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Peer-to-Peer (P2P) cloud systems are becoming more popular due to the high computational capability, scalability, reliability, and efficient data sharing. However, sending and receiving a massive amount of data causes huge network traffic leading to significant communication delays. In P2P systems, a considerable amount of the mentioned traffic and delay is owing to the mismatch between the physical layer and the overlay layer, which is referred to as locality problem. To achieve higher performance and consequently resilience to failures, each peer has to make connections to geographically closer peers. To the best of our knowledge, locality problem is not considered in any well known P2P cloud system. However, considering this problem could enhance the overall network performance by shortening the response time and decreasing the overall network traffic. In this paper, we propose a novel, efficient, and general solution for locality problem in P2P cloud systems considering the round-trip-time (RTT). Furthermore, we suggest a flexible topology as the overlay graph to address the locality problem more effectively. Comprehensive simulation experiments are conducted to demonstrate the applicability of the proposed algorithm in most of the well-known P2P overlay networks while not introducing any serious overhead.
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KAWASHIMA, Ryota, and Hiroshi MATSUO. "Non-tunneling Overlay Approach for Virtual Tenant Networks in Cloud Datacenter." IEICE Transactions on Communications E97.B, no. 11 (2014): 2259–68. http://dx.doi.org/10.1587/transcom.e97.b.2259.
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Barabash, Kathy, David Breitgand, Etai Lev-Ran, Dean H. Lorenz, and Danny Raz. "A case for an open customizable cloud network." ACM SIGCOMM Computer Communication Review 52, no. 2 (April 30, 2022): 56–62. http://dx.doi.org/10.1145/3544912.3544919.
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Cloud computing is transforming networking landscape over the last few years. The first order of business for major cloud providers today is to attract as many organizations as possible to their own clouds. To that end cloud providers offer a new generation of managed network solutions to connect the premises of the enterprises to their clouds. To serve their customers better and to innovate fast, major cloud providers are currently on the route to building their own "private Internets", which are idiosyncratic. On the other hand, customers that do not want to stay locked by vendors and who want flexibility in using best-for-the-task services spanning multiple clouds and, possibly, their own premises, seek for solutions that will provide smart overlay connectivity across clouds. The result of these developments is a multiplication of closed idiosyncratic solutions rather than an open standardized ecosystem. In this editorial note we argue for desirability of such an ecosystem, outline the main requirements and sketch possible solutions. We focus on enterprise as our primary use case and illustrate the main ideas through it, but the same principles apply to various different use cases.
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Pham, Van-Nam, VanDung Nguyen, Tri D. T. Nguyen, and Eui-Nam Huh. "Efficient Edge-Cloud Publish/Subscribe Broker Overlay Networks to Support Latency-Sensitive Wide-Scale IoT Applications." Symmetry 12, no. 1 (December 18, 2019): 3. http://dx.doi.org/10.3390/sym12010003.
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Computing services for the Internet-of-Things (IoT) play a vital role for widespread IoT deployment. A hierarchy of Edge-Cloud publish/subscribe (pub/sub) broker overlay networks that support latency-sensitive IoT applications in a scalable manner is introduced. In addition, we design algorithms to cluster edge pub/sub brokers based on topic similarities and geolocations to enhance data dissemination among end-to-end IoT devices. The proposed model is designed to provide low delay data dissemination and effectively save network traffic among brokers. In the proposed model, IoT devices running pub/sub client applications periodically send collected data, organized as a hierarchy of topics, to their closest edge pub/sub brokers. Then, the data are processed/analyzed at edge nodes to make controlling decisions promptly replying to the IoT devices and/or aggregated for further delivery to other interested edge brokers or to cloud brokers for long-term processing, analysis, and storage. Extensive simulation results demonstrate that our proposal achieves the best data delivery latency compared to two baseline schemes, a classical Cloud-based pub/sub scheme and an Edge-Cloud pub/sub scheme. Considering the similar Edge-Cloud technique, the proposed scheme outperforms PubSubCoord-alike in terms of relay traffic ratio among brokers. Therefore, our proposal can adapt well to support wide-scale latency-sensitive IoT applications.
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Qian, He, Wang Yong, Li Jia, and Cai Mengfei. "Publish/Subscribe and JXTA based Cloud Service Management with QoS." International Journal of Grid and High Performance Computing 8, no. 3 (July 2016): 24–37. http://dx.doi.org/10.4018/ijghpc.2016070102.
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How to manage cloud services efficiently is difficult for large scale of services with frequently changing Quality of Service (QoS) in cloud computing environment. A multiple-dimension publish/subscribe (pub/sub) and JXTA based cloud service management mechanism, consists of registry overlay, service publisher and subscriber, is proposed to manage cloud services with active QoS refreshing and fast subscribe capability. The registry overlay with multiple managers cooperating on JXTA, can manage large scale services discovery. The service model with QoS describes a formal model for pub/sub based service management, and a fast subscribing algorithm with filter matrix and multi-dimension index is proposed. The filter matrix helps to reduce candidate services and the multi-dimension index is used to find satisfied services fast. Based on pub/sub and JXTA, the cloud management system is realized. The experiments show that the proposed cloud service management mechanism has good publication and subscribing performance, and is faster than traditional methods for large scale cloud services.
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Li, Yanjun, Guoqing Zhang, and Guoqiang Zhang. "ISP-Friendly Data Scheduling by Advanced Locality-Aware Network Coding for P2P Distribution Cloud." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/968328.
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Peer-to-peer (P2P) file distribution imposes increasingly heavy traffic burden on the Internet service providers (ISPs). The vast volume of traffic pushes up ISPs’ costs in routing and investment and degrades their networks performance. Building ISP-friendly P2P is therefore of critical importance for ISPs and P2P services. So far most efforts in this area focused on improving the locality-awareness of P2P applications, for example, to construct overlay networks with better knowledge of the underlying network topology. There is, however, growing recognition that data scheduling algorithms also play an effective role in P2P traffic reduction. In this paper, we introduce the advanced locality-aware network coding (ALANC) for P2P file distribution. This data scheduling algorithm completely avoids the transmission of linearly dependent data blocks, which is a notable problem of previous network coding algorithms. Our simulation results show that, in comparison to other algorithms, ALANC not only significantly reduces interdomain P2P traffic, but also remarkably improves both the application-level performance (for P2P services) and the network-level performance (for ISP networks). For example, ALANC is 30% faster in distributing data blocks and it reduces the average traffic load on the underlying links by 40%. We show that ALANC holds the above gains when the tit-for-tat incentive mechanism is introduced or the overlay topology changes dynamically.
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Andre, Jean-Marc, Ulf Behrens, James Branson, Philipp Brummer, Olivier Chaze, Sergio Cittolin, Diego da Silva Gomes, et al. "Experience with dynamic resource provisioning of the CMS online cluster using a cloud overlay." EPJ Web of Conferences 214 (2019): 07017. http://dx.doi.org/10.1051/epjconf/201921407017.
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The primary goal of the online cluster of the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) is to build event data from the detector and to select interesting collisions in the High Level Trigger (HLT) farm for offline storage. With more than 1500 nodes and a capacity of about 850 kHEPSpecInt06, the HLT machines represent similar computing capacity of all the CMS Tier1 Grid sites together. Moreover, it is currently connected to the CERN IT datacenter via a dedicated 160 Gbps network connection and hence can access the remote EOS based storage with a high bandwidth. In the last few years, a cloud overlay based on OpenStack has been commissioned to use these resources for the WLCG when they are not needed for data taking. This online cloud facility was designed for parasitic use of the HLT, which must never interfere with its primary function as part of the DAQ system. It also allows to abstract from the different types of machines and their underlying segmented networks. During the LHC technical stop periods, the HLT cloud is set to its static mode of operation where it acts like other grid facilities. The online cloud was also extended to make dynamic use of resources during periods between LHC fills. These periods are a-priori unscheduled and of undetermined length, typically of several hours, once or more a day. For that, it dynamically follows LHC beam states and hibernates Virtual Machines (VM) accordingly. Finally, this work presents the design and implementation of a mechanism to dynamically ramp up VMs when the DAQ load on the HLT reduces towards the end of the fill.
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Wang, Lan. "THE RANDOM NEURAL NETWORK FOR COGNITIVE TRAFFIC ROUTING AND TASK ALLOCATION IN NETWORKS AND THE CLOUD." Probability in the Engineering and Informational Sciences 31, no. 4 (May 22, 2017): 540–60. http://dx.doi.org/10.1017/s0269964817000183.
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G-Network queueing network models, and in particular the random neural network (RNN), are useful tools for decision making in complex systems, due to their ability to learn from measurements in real time, and in turn provide real-time decisions regarding resource and task allocation. In particular, the RNN has led to the design of the cognitive packet network (CPN) decision tool for the routing of packets in the Internet, and for task allocation in the Cloud. Thus in this paper, we present recent research on how to dynamically create the means for quality of service (QoS) to end users of the Internet and in the Cloud. The approach is based on adapting the decisions so as to benefit users as the conditions in the Internet and in Cloud servers vary due to changing traffic and workload. We present an overview of the algorithms that were designed based on the RNN, and also detail the experimental results that were obtained in three areas: (i) traffic routing for real-time applications, which have strict QoS constraints; (ii) routing approaches, which operate at the overlay level without affecting the Internet infrastructure; and (iii) the routing of tasks across servers in the Cloud through the Internet.
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Benomar, Zakaria, Francesco Longo, Giovanni Merlino, and Antonio Puliafito. "Cloud-based Network Virtualization in IoT with OpenStack." ACM Transactions on Internet Technology 22, no. 1 (February 28, 2022): 1–26. http://dx.doi.org/10.1145/3460818.
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In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.
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Fu, Chunle, Bailing Wang, Hongri Liu, and Wei Wang. "Software-Defined Virtual Private Network for SD-WAN." Electronics 13, no. 13 (July 8, 2024): 2674. http://dx.doi.org/10.3390/electronics13132674.
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Software-Defined Wide Area Networks (SD-WANs) are an emerging Software-Defined Network (SDN) technology to reinvent Wide Area Networks (WANs) for ubiquitous network interconnections in cloud computing, edge computing, and the Internet of Everything. The state-of-the-art overlay-based SD-WANs are simply conjunctions of Virtual Private Network (VPN) and SDN architecture to leverage the controllability and programmability of SDN, which are only applicable for specific platforms and do not comply with the extensibility of SDN. This paper motivates us to refactor traditional VPNs with SDN architecture by proposing an overlay-based SD-WAN solution named Software-Defined Virtual Private Network (SD-VPN). An SDN-based auto-constructed VPN model and its evaluating metrics are put forward to automatically construct overlay WANs by node placement and service orchestration of SD-VPN. Therefore, a joint placement algorithm of VPN nodes and algorithms for overlay WAN service loading and offloading are proposed for SD-VPN controllers. Finally, a three-layer SD-VPN system is implemented and deployed in actual network environments. Simulation experiments and system tests are conducted to prove the high-efficiency controllability, real-time programmability, and auto-constructed deployability of the proposed SD-VPN. Performance trade-off between SD-VPN control channels and data channels is evaluated, and SD-VPN controllers are proven to be extensible for other VPN protocols and advanced services.
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Xia, Lei, Zheng Cui, John Lange, Yuan Tang, Peter Dinda, and Patrick Bridges. "Fast VMM-based overlay networking for bridging the cloud and high performance computing." Cluster Computing 17, no. 1 (May 30, 2013): 39–59. http://dx.doi.org/10.1007/s10586-013-0274-7.
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Paiker, Nafize Rabbani, Jianchen Shan, Cristian Borcea, Narain Gehani, Reza Curtmola, and Xiaoning Ding. "Design and Implementation of an Overlay File System for Cloud-Assisted Mobile Apps." IEEE Transactions on Cloud Computing 8, no. 1 (January 1, 2020): 97–111. http://dx.doi.org/10.1109/tcc.2017.2763158.
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Hossen, Rakib, Md Whaiduzzaman, Mohammed Nasir Uddin, Md Jahidul Islam, Nuruzzaman Faruqui, Alistair Barros, Mehdi Sookhak, and Md Julkar Nayeen Mahi. "BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration." Information 12, no. 12 (December 10, 2021): 517. http://dx.doi.org/10.3390/info12120517.
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The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.
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He, Tao, Kunxin Zhu, Zhipeng Chen, Ruomei Wang, and Fan Zhou. "Popularity-Guided Cost Optimization for Live Streaming in Mobile Edge Computing." Wireless Communications and Mobile Computing 2022 (January 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/5562995.
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Live streaming service usually delivers the content in mobile edge computing (MEC) to reduce the network latency and save the backhaul capacity. Considering the limited resources, it is necessary that MEC servers collaborate with each other and form an overlay to realize more efficient delivery. The critical challenge is how to optimize the topology among the servers and allocate the link capacity so that the cost will be lower with delay constraints. Previous approaches rarely consider server collaborations for live streaming service, and the scheduling delay is usually ignored in MEC, leading to suboptimal performances. In this paper, we propose a popularity-guided overlay model which takes the scheduling delay into consideration and utilizes MEC collaboration to achieve efficient live streaming service. The links and servers are shared among all channel streams and each stream is pushed from cloud servers to MEC servers via the trees. Considering the optimization problem is NP-hard, we propose an effective optimization framework called cost optimization for live streaming (COLS) to predict the channel popularity by a LSTM model with multiscale input data. Finally, we compute topology graph by greedy scheme and allocate the capacity with convex programming. Experimental results show that the proposed approach achieves higher prediction accuracy, reducing the capacity cost by more than 40% with an acceptable delay compared with state-of-the-art schemes.
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Zhu, Wen, Tianliang Chen, Beiping Hou, Chen Bian, Aihua Yu, Lingchao Chen, Ming Tang, and Yuzhen Zhu. "Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network." Applied Sciences 12, no. 3 (February 1, 2022): 1570. http://dx.doi.org/10.3390/app12031570.
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Changes in clouds can affect the outpower of photovoltaics (PVs). Ground-based cloud images classification is an important prerequisite for PV power prediction. Due to the intra-class difference and inter-class similarity of cloud images, the classical convolutional network is obviously insufficient in distinguishing ability. In this paper, a classification method of ground-based cloud images by improved combined convolutional network is proposed. To solve the problem of sub-network overfitting caused by redundancy of pixel information, overlap pooling kernel is used to enhance the elimination effect of information redundancy in the pooling layer. A new channel attention module, ECA-WS (Efficient Channel Attention–Weight Sharing), is introduced to improve the network’s ability to express channel information. The decision fusion algorithm is employed to fuse the outputs of sub-networks with multi-scales. According to the number of cloud images in each category, different weights are applied to the fusion results, which solves the problem of network scale limitation and dataset imbalance. Experiments are carried out on the open MGCD dataset and the self-built NRELCD dataset. The results show that the proposed model has significantly improved the classification accuracy compared with the classical network and the latest algorithms.
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Zhang, Jiazhe, Xingwei Li, Xianfa Zhao, and Zheng Zhang. "LLGF-Net: Learning Local and Global Feature Fusion for 3D Point Cloud Semantic Segmentation." Electronics 11, no. 14 (July 13, 2022): 2191. http://dx.doi.org/10.3390/electronics11142191.
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Three-dimensional (3D) point cloud semantic segmentation is fundamental in complex scene perception. Currently, although various efficient 3D semantic segmentation networks have been proposed, the overall effect has a certain gap to 2D image segmentation. Recently, some transformer-based methods have opened a new stage in computer vision, which also has accelerated the effective development of methods in 3D point cloud segmentation. In this paper, we propose a novel semantic segmentation network named LLGF-Net that can aggregate features from both local and global levels of point clouds, effectively improving the ability to extract feature information from point clouds. Specifically, we adopt the multi-head attention mechanism in the original Transformer model to obtain the local features of point clouds and then use the position-distance information of point clouds in 3D space to obtain the global features. Finally, the local features and global features are fused and embedded into the encoder–decoder network to generate our method. Our extensive experimental results on the 3D point cloud dataset demonstrate the effectiveness and superiority of our method.
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Hao, Ruidong, Zhongwei Wei, Xu He, Kaifeng Zhu, Jiawei He, Jun Wang, Muyu Li, et al. "Robust Point Cloud Registration Network for Complex Conditions." Sensors 23, no. 24 (December 15, 2023): 9837. http://dx.doi.org/10.3390/s23249837.
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Point cloud registration is widely used in autonomous driving, SLAM, and 3D reconstruction, and it aims to align point clouds from different viewpoints or poses under the same coordinate system. However, point cloud registration is challenging in complex situations, such as a large initial pose difference, high noise, or incomplete overlap, which will cause point cloud registration failure or mismatching. To address the shortcomings of the existing registration algorithms, this paper designed a new coarse-to-fine registration two-stage point cloud registration network, CCRNet, which utilizes an end-to-end form to perform the registration task for point clouds. The multi-scale feature extraction module, coarse registration prediction module, and fine registration prediction module designed in this paper can robustly and accurately register two point clouds without iterations. CCRNet can link the feature information between two point clouds and solve the problems of high noise and incomplete overlap by using a soft correspondence matrix. In the standard dataset ModelNet40, in cases of large initial pose difference, high noise, and incomplete overlap, the accuracy of our method, compared with the second-best popular registration algorithm, was improved by 7.0%, 7.8%, and 22.7% on the MAE, respectively. Experiments showed that our CCRNet method has advantages in registration results in a variety of complex conditions.
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Yang, Chaoyun, Yonghong Zhang, Min Xia, Haifeng Lin, Jia Liu, and Yang Li. "Satellite Image for Cloud and Snow Recognition Based on Lightweight Feature Map Attention Network." ISPRS International Journal of Geo-Information 11, no. 7 (July 12, 2022): 390. http://dx.doi.org/10.3390/ijgi11070390.
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Cloud and snow recognition technology is of great significance in the field of meteorology, and is also widely used in remote sensing mapping, aerospace, and other fields. Based on the traditional method of manually labeling cloud-snow areas, a method of labeling cloud and snow areas using deep learning technology has been gradually developed to improve the accuracy and efficiency of recognition. In this paper, from the perspective of designing an efficient and lightweight network model, a cloud snow recognition model based on a lightweight feature map attention network (Lw-fmaNet) is proposed to ensure the performance and accuracy of the cloud snow recognition model. The model is improved based on the ResNet18 network with the premise of reducing the network parameters and improving the training efficiency. The main structure of the model includes a shallow feature extraction module, an intrinsic feature mapping module, and a lightweight adaptive attention mechanism. Overall, in the experiments conducted in this paper, the accuracy of the proposed cloud and snow recognition model reaches 95.02%, with a Kappa index of 93.34%. The proposed method achieves an average precision rate of 94.87%, an average recall rate of 94.79%, and an average F1-Score of 94.82% for four sample recognition classification tasks: no snow and no clouds, thin cloud, thick cloud, and snow cover. Meanwhile, our proposed network has only 5.617M parameters and takes only 2.276 s. Compared with multiple convolutional neural networks and lightweight networks commonly used for cloud and snow recognition, our proposed lightweight feature map attention network has a better performance when it comes to performing cloud and snow recognition tasks.
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Li, Xiaolong, Hong Zheng, Chuanzhao Han, Wentao Zheng, Hao Chen, Ying Jing, and Kaihan Dong. "SFRS-Net: A Cloud-Detection Method Based on Deep Convolutional Neural Networks for GF-1 Remote-Sensing Images." Remote Sensing 13, no. 15 (July 24, 2021): 2910. http://dx.doi.org/10.3390/rs13152910.
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Clouds constitute a major obstacle to the application of optical remote-sensing images as they destroy the continuity of the ground information in the images and reduce their utilization rate. Therefore, cloud detection has become an important preprocessing step for optical remote-sensing image applications. Due to the fact that the features of clouds in current cloud-detection methods are mostly manually interpreted and the information in remote-sensing images is complex, the accuracy and generalization of current cloud-detection methods are unsatisfactory. As cloud detection aims to extract cloud regions from the background, it can be regarded as a semantic segmentation problem. A cloud-detection method based on deep convolutional neural networks (DCNN)—that is, a spatial folding–unfolding remote-sensing network (SFRS-Net)—is introduced in the paper, and the reason for the inaccuracy of DCNN during cloud region segmentation and the concept of space folding/unfolding is presented. The backbone network of the proposed method adopts an encoder–decoder structure, in which the pooling operation in the encoder is replaced by a folding operation, and the upsampling operation in the decoder is replaced by an unfolding operation. As a result, the accuracy of cloud detection is improved, while the generalization is guaranteed. In the experiment, the multispectral data of the GaoFen-1 (GF-1) satellite is collected to form a dataset, and the overall accuracy (OA) of this method reaches 96.98%, which is a satisfactory result. This study aims to develop a method that is suitable for cloud detection and can complement other cloud-detection methods, providing a reference for researchers interested in cloud detection of remote-sensing images.
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Piontek, Dennis, Luca Bugliaro, Marius Schmidl, Daniel K. Zhou, and Christiane Voigt. "The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development." Remote Sensing 13, no. 16 (August 6, 2021): 3112. http://dx.doi.org/10.3390/rs13163112.
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Volcanic ash clouds are a threat to air traffic security and, thus, can have significant societal and financial impact. Therefore, the detection and monitoring of volcanic ash clouds to enhance the safety of air traffic is of central importance. This work presents the development of the new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) which is based on artificial neural networks, the thermal channels of the geostationary sensor MSG/SEVIRI and auxiliary data from a numerical weather prediction model. It derives a pixel classification as well as cloud top height, effective particle radius and, indirectly, the mass column concentration of volcanic ash clouds during day and night. A large set of realistic one-dimensional radiative transfer calculations for typical atmospheric conditions with and without generic volcanic ash clouds is performed to create the training dataset. The atmospheric states are derived from ECMWF data to cover the typical diurnal, annual and interannual variability. The dependence of the surface emissivity on surface type and viewing zenith angle is considered. An extensive dataset of volcanic ash optical properties is used, derived for a wide range of microphysical properties and refractive indices of various petrological compositions, including different silica contents and glass-to-crystal ratios; this constitutes a major innovation of this retrieval. The resulting ash-free radiative transfer calculations at a specific time compare well with corresponding SEVIRI measurements, considering the individual pixel deviations as well as the overall brightness temperature distributions. Atmospheric gas profiles and sea surface emissivities are reproduced with a high agreement, whereas cloudy cases can show large deviations on a single pixel basis (with 95th percentiles of the absolute deviations > 30 K), mostly due to different cloud properties in model and reality. Land surfaces lead to large deviations for both the single pixel comparison (with median absolute deviations > 3 K) and more importantly the brightness temperature distributions, most likely due to imprecise skin temperatures. The new method enables volcanic ash-related scientific investigations as well as aviation security-related applications.
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Singer, Nina, and Vijayan K. Asari. "View-Agnostic Point Cloud Generation for Occlusion Reduction in Aerial Lidar." Remote Sensing 14, no. 13 (June 21, 2022): 2955. http://dx.doi.org/10.3390/rs14132955.
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Occlusions are one of the leading causes of data degradation in lidar. The presence of occlusions reduces the overall aesthetic quality of a point cloud, creating a signature that is specific to that viewpoint and sensor modality. Typically, datasets consist of a series of point clouds with one type of sensor and a limited range of viewpoints. Therefore, when training a dataset with a particular signature, it is challenging to infer scenes outside of the original range of the viewpoints from the training dataset. This work develops a generative network that can predict the area in which an occlusion occurs and furnish the missing points. The output is a complete point cloud that is a more general representation and agnostic to the original viewpoint. We can then use the resulting point cloud as an input for a secondary method such as semantic or instance segmentation. We propose a learned sampling technique that uses the features to inform the point sampling instead of relying strictly on spatial information. We also introduce a new network structure that considers multiple point locations and augmentations to generate parallel features. The network is tested against other methods using our aerial occlusion dataset, DALES Viewpoints Version 2, and also against other point cloud completion networks on the Point Cloud Network (PCN) dataset. We show that it reduces occlusions visually and outperforms state-of-the-art point cloud completion networks in both Chamfers and Earth Mover’s Distance (EMD) metrics. We also show that using our occlusion reduction method as a pre-processing step improves semantic segmentation results compared to the same scenes processed without using our method.
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Li, Wangbin, Kaimin Sun, Zhuotong Du, Xiuqing Hu, Wenzhuo Li, Jinjiang Wei, and Song Gao. "PCNet: Cloud Detection in FY-3D True-Color Imagery Using Multi-Scale Pyramid Contextual Information." Remote Sensing 13, no. 18 (September 14, 2021): 3670. http://dx.doi.org/10.3390/rs13183670.
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Cloud, one of the poor atmospheric conditions, significantly reduces the usability of optical remote-sensing data and hampers follow-up applications. Thus, the identification of cloud remains a priority for various remote-sensing activities, such as product retrieval, land-use/cover classification, object detection, and especially for change detection. However, the complexity of clouds themselves make it difficult to detect thin clouds and small isolated clouds. To accurately detect clouds in satellite imagery, we propose a novel neural network named the Pyramid Contextual Network (PCNet). Considering the limited applicability of a regular convolution kernel, we employed a Dilated Residual Block (DRB) to extend the receptive field of the network, which contains a dilated convolution and residual connection. To improve the detection ability for thin clouds, the proposed new model, pyramid contextual block (PCB), was used to generate global information at different scales. FengYun-3D MERSI-II remote-sensing images covering China with 14,165 × 24,659 pixels, acquired on 17 July 2019, are processed to conduct cloud-detection experiments. Experimental results show that the overall precision rates of the trained network reach 97.1% and the overall recall rates reach 93.2%, which performs better both in quantity and quality than U-Net, UNet++, UNet3+, PSPNet and DeepLabV3+.
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Chen, Yi, Yong Wang, Jinlong Li, Yu Zhang, and Xiaorong Gao. "A Partial-to-Partial Point Cloud Registration Method Based on Geometric Attention Network." Journal of Sensors 2023 (October 27, 2023): 1–12. http://dx.doi.org/10.1155/2023/3427758.
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Partial point cloud registration is an important step in generating a full 3D model. Many deep learning-based methods show good performance for the registration of complete point clouds but cannot deal with the registration of partial point clouds effectively. Recent methods that seek correspondences over downsampled superpoints show great potential in partial point cloud registration. Therefore, this paper proposes a partial-to-partial point cloud registration network based on geometric attention (GAP-Net), which mainly includes a backbone network optimized by a spatial attention module and an overlapping attention module guided by geometric information. The former aggregates the feature information of superpoints, and the latter focuses on superpoint matching in overlapping regions. The experimental results show that the method achieves better registration performance on ModelNet and ModelLoNet with lower overlap. The rotation error is reduced by 14.49% and 17.12%, respectively, which is robust to the overlap rate.
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Zhai, Ruifeng, Junfeng Song, Shuzhao Hou, Fengli Gao, and Xueyan Li. "Self-Supervised Learning for Point-Cloud Classification by a Multigrid Autoencoder." Sensors 22, no. 21 (October 23, 2022): 8115. http://dx.doi.org/10.3390/s22218115.
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It has become routine to directly process point clouds using a combination of shared multilayer perceptrons and aggregate functions. However, this practice has difficulty capturing the local information of point clouds, leading to information loss. Nevertheless, several recent works have proposed models that establish point-to-point relationships based on this procedure. However, to address the information loss, in this study we use self-supervised methods to enhance the network’s understanding of point clouds. Our proposed multigrid autoencoder (MA) constrains the encoder part of the classification network so that it gains an understanding of the point cloud as it reconstructs it. With the help of self-supervised learning, we find the original network improves performance. We validate our model on PointNet++, and the experimental results show that our method improves overall classification accuracy by 2.0% and 4.7% with ModelNet40 and ScanObjectNN datasets, respectively.
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Zhou, Xin. "RICE: A Dataset and Baseline for Cloud Removal in Remote Sensing Images." Journal of Combinatorial Mathematics and Combinatorial Computing 120, no. 1 (June 30, 2024): 107–24. http://dx.doi.org/10.61091/jcmcc120-10.
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Removing clouds is an essential preprocessing step in analyzing remote sensing images, as cloud-based overlays commonly occur in optical remote sensing images and can significantly limit the usability of the acquired data. Deep learning has exhibited remarkable progress in remote sensing, encompassing scene classification and change detection tasks. Nevertheless, the appli-cation of deep learning techniques to cloud removal in remote sensing images is currently con-strained by the limited availability of training datasets explicitly tailored for neural networks. This paper presents the Remote sensing Image Cloud rEmoving dataset (RICE) to address this challenge and proposes baseline models incorporating a convolutional attention mechanism, which has demonstrated superior performance in identifying and restoring cloud-affected regions, with quantitative results indicating a 3.08% improvement in accuracy over traditional methods. This mechanism empowers the network to comprehend better the spatial structure, local details, and inter-channel correlations within remote sensing images, thus effectively addressing the diverse distributions of clouds. Moreover, by integrating this attention mechanism, our models achieve a crucial comparison advantage, outperforming existing state-of-the-art techniques in terms of both visual quality and quantitative metrics. We propose adopting the Learned Per-ceptual Image Patch Similarity metric, which emphasizes perceptual similarity, to evaluate the quality of cloud-free images generated by the models. Our work not only contributes to advancing cloud removal techniques in remote sensing but also provides a comprehensive evaluation framework for assessing the fidelity of the generated images.
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Li, Jiang, and Jinhao Liu. "PointDMS: An Improved Deep Learning Neural Network via Multi-Feature Aggregation for Large-Scale Point Cloud Segmentation in Smart Applications of Urban Forestry Management." Forests 14, no. 11 (October 31, 2023): 2169. http://dx.doi.org/10.3390/f14112169.
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Background: The development of laser measurement techniques is of great significance in forestry monitoring and park management in smart cities. It provides many conveniences for improving landscape planning efficiency and strengthening digital construction. However, capturing 3D point clouds in large-scale landscape environments is a complex task that generates massive amounts of unstructured data with characteristics such as randomness, rotational invariance, sparsity, and serious barriers. Methods: To improve the processing efficiency of intelligent devices for massive point clouds, we propose a novel deep learning neural network based on a multi-feature aggregation strategy. This network is designed to divide 3D laser point clouds in complex large-scale scenarios. Firstly, we utilize multiple terrestrial laser sensors to collect a large amount of data in open scenes such as parks, streets, and forests in urban environments. These data are integrated into a practical database called DMSdataset, which contains different information variables, densities, and dimensions. Then, an automatic block integrated with a multi-feature extractor is constructed to pre-process the unstructured point cloud data and standardize the datasets. Finally, a novel semantic segmentation framework called PointDMS is designed using 3D convolutional deep networks. PointDMS achieves a better segmentation performance of point clouds with a lightweight parameter structure. Here, “D” stands for deep network, “M” stands for multi-feature, and “S” stands for segmentation. Results: Extensive experiments on self-built datasets show that the proposed PointDMS achieves similar or better performance in point cloud segmentation compared to other methods. The overall identification accuracy of the proposed model is up to 93.5%, which is a 14% increase. Particularly for living wood objects, the average identification accuracy is up to 88.7%, which is, at least, an 8.2% increase. These results effectively prove that PointDMS is beneficial for 3D point cloud processing, division, and mining applications in urban forest environments. It demonstrates good robustness and generalization.
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Chen, Yonghua, Filipe Aires, Jennifer A. Francis, and James R. Miller. "Observed Relationships between Arctic Longwave Cloud Forcing and Cloud Parameters Using a Neural Network." Journal of Climate 19, no. 16 (August 15, 2006): 4087–104. http://dx.doi.org/10.1175/jcli3839.1.
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Abstract A neural network technique is used to quantify relationships involved in cloud–radiation feedbacks based on observations from the Surface Heat Budget of the Arctic (SHEBA) project. Sensitivities of longwave cloud forcing (CFL) to cloud parameters indicate that a bimodal distribution pattern dominates the histogram of each sensitivity. Although the mean states of the relationships agree well with those derived in a previous study, they do not often exist in reality. The sensitivity of CFL to cloud cover increases as the cloudiness increases with a range of 0.1–0.9 W m−2 %−1. There is a saturation effect of liquid water path (LWP) on CFL. The highest sensitivity of CFL to LWP corresponds to clouds with low LWP, and sensitivity decreases as LWP increases. The sensitivity of CFL to cloud-base height (CBH) depends on whether the clouds are below or above an inversion layer. The relationship is negative for clouds higher than 0.8 km at the SHEBA site. The strongest positive relationship corresponds to clouds with low CBH. The dominant mode of the sensitivity of CFL to cloud-base temperature (CBT) is near zero and corresponds to warm clouds with base temperatures higher than −9°C. The low and high sensitivity regimes correspond to the summer and winter seasons, respectively, especially for LWP and CBT. Overall, the neural network technique is able to separate two distinct regimes of clouds that correspond to different sensitivities; that is, it captures the nonlinear behavior in the relationships. This study demonstrates a new method for evaluating nonlinear relationships between climate variables. It could also be used as an effective tool for evaluating feedback processes in climate models.
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du Piesanie, A., A. J. M. Piters, I. Aben, H. Schrijver, P. Wang, and S. Noël. "Validation of two independent retrievals of SCIAMACHY water vapour columns using radiosonde data." Atmospheric Measurement Techniques Discussions 6, no. 1 (January 21, 2013): 665–702. http://dx.doi.org/10.5194/amtd-6-665-2013.
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Abstract. Two independently derived SCIAMACHY total water vapour column (WVC) products are compared with integrated water vapour data calculated from radiosonde measurements, and with each other. The two SCIAMACHY WVC products are retrieved with two different retrieval algorithms applied in the visible and short wave infrared wavelength regions respectively. The first SCIAMACHY WVC product used in the comparison is ESA's level 2 version 5.01 WVC product derived with the Air Mass Corrected Differential Absorption Spectroscopy (AMC-DOAS) retrieval algorithm (SCIAMACHY-ESA). The second SCIAMACHY WVC product is derived using the Iterative Maximum Likelihood Method (IMLM) developed by Netherlands Institute for Space Research (SCIAMACHY-IMLM). Both SCIAMACHY WVC products are compared with collocated water vapour amounts determined from daily relative humidity radiosonde measurements obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) radiosonde network, over an 18 month and 2 yr period respectively. Results indicate a good agreement between the WVC amounts of SCIAMACHY-ESA and the radiosonde, and a mean difference of 0.03 g cm−2 is found for cloud free conditions. Overall the SCIAMACHY-ESA WVC amounts are smaller than the radiosonde WVC amounts, especially over oceans. For cloudy conditions the WVC bias has a clear dependence on the cloud top height and increases with increasing cloud top heights larger than approximately 2 km. A likely cause for this could be the different vertical profile shapes of water vapour and O2 leading to different relative changes in their optical thickness, which makes the AMF correction method used in the algorithm less suitable for high clouds. The SCIAMACHY-IMLM WVC amounts compare well to the radiosonde WVC amounts during cloud free conditions over land. A mean difference of 0.08 g cm−2 is found which is consistent with previous results when comparing daily averaged SCIAMACHY-IMLM WVC amounts with ECMWF model data globally. Furthermore, we show that the measurements for cloudy conditions (cloud fraction ≥ 0.5) with low clouds (cloud pressure ≥ 930 hPa) above the ocean and land compare quite well with radiosonde data.
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Glazyrina, Natalya, Raikhan Muratkhan, Serik Eslyamov, Gulden Murzabekova, Nurgul Aziyeva, Bakhytgul Rysbekkyzy, Ainur Orynbayeva, and Nazira Baktiyarova. "Deep neural networks for removing clouds and nebulae from satellite images." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 5 (October 1, 2024): 5390. http://dx.doi.org/10.11591/ijece.v14i5.pp5390-5399.
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This research paper delves into contemporary methodologies for eradicating clouds and nebulae from space images utilizing advanced deep learning technologies such as conditional generative adversarial networks (conditional GAN), cyclic generative adversarial networks (CycleGAN), and space-attention generative adversarial networks (space-attention GAN). Cloud cover presents a significant obstacle in remote sensing, impeding accurate data analysis across various domains including environmental monitoring and natural resource management. The proposed techniques offer novel solutions by leveraging spatial attention mechanisms to identify and subsequently eliminate clouds from images, thus uncovering previously concealed information and enhancing the quality of space data. The study emphasizes the necessity for further research aimed at refining cloud removal algorithms to accommodate diverse detection conditions and enhancing the overall efficiency of deep learning in satellite image processing. By highlighting potential benefits and advocating for ongoing exploration, the paper underscores the importance of advancing cloud removal techniques to improve data quality and unlock new applications in Earth remote sensing. In conclusion, the proposed approaches hold promise in addressing the persistent challenge of cloud cover in space imagery, paving the way for more accurate data analysis and future advancements in remote sensing technologies.
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Sarkar, Sirshak, Gaurav Choudhary, Shishir Kumar Shandilya, Azath Hussain, and Hwankuk Kim. "Security of Zero Trust Networks in Cloud Computing: A Comparative Review." Sustainability 14, no. 18 (September 7, 2022): 11213. http://dx.doi.org/10.3390/su141811213.
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Recently, networks have shifted from traditional in-house servers to third-party-managed cloud platforms due to its cost-effectiveness and increased accessibility toward its management. However, the network remains reactive, with less accountability and oversight of its overall security. Several emerging technologies have restructured our approach to the security of cloud networks; one such approach is the zero-trust network architecture (ZTNA), where no entity is implicitly trusted in the network, regardless of its origin or scope of access. The network rewards trusted behaviour and proactively predicts threats based on its users’ behaviour. The zero-trust network architecture is still at a nascent stage, and there are many frameworks and models to follow. The primary focus of this survey is to compare the novel requirement-specific features used by state-of-the-art research models for zero-trust cloud networks. In this manner, the features are categorized across nine parameters into three main types: zero-trust-based cloud network models, frameworks and proofs-of-concept. ZTNA, when wholly realized, enables network administrators to tackle critical issues such as how to inhibit internal and external cyber threats, enhance the visibility of the network, automate the calculation of trust for network entities and orchestrate security for users. The paper further focuses on domain-specific issues plaguing modern cloud computing networks, which leverage choosing and implementing features necessary for future networks and incorporate intelligent security orchestration, automation and response. The paper also discusses challenges associated with cloud platforms and requirements for migrating to zero-trust architecture. Finally, possible future research directions are discussed, wherein new technologies can be incorporated into the ZTA to build robust trust-based enterprise networks deployed in the cloud.
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Zhang, Chunjiao, Shenghua Xu, Tao Jiang, Jiping Liu, Zhengjun Liu, An Luo, and Yu Ma. "Integrating Normal Vector Features into an Atrous Convolution Residual Network for LiDAR Point Cloud Classification." Remote Sensing 13, no. 17 (August 29, 2021): 3427. http://dx.doi.org/10.3390/rs13173427.
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LiDAR point clouds are rich in spatial information and can effectively express the size, shape, position, and direction of objects; thus, they have the advantage of high spatial utilization. The point cloud focuses on describing the shape of the external surface of the object itself and will not store useless redundant information to describe the occupation. Therefore, point clouds have become the research focus of 3D data models and are widely used in large-scale scene reconstruction, virtual reality, digital elevation model production, and other fields. Since point clouds have various characteristics, such as disorder, density inconsistency, unstructuredness, and incomplete information, point cloud classification is still complex and challenging. To realize the semantic classification of LiDAR point clouds in complex scenarios, this paper proposes the integration of normal vector features into an atrous convolution residual network. Based on the RandLA-Net network structure, the proposed network integrates the atrous convolution into the residual module to extract global and local features of the point clouds. The atrous convolution can learn more valuable point cloud feature information by expanding the receptive field. Then, the point cloud normal vector is embedded in the local feature aggregation module of the RandLA-Net network to extract local semantic aggregation features. The improved local feature aggregation module can merge the deep features of the point cloud and mine the fine-grained information of the point cloud to improve the model’s segmentation ability in complex scenes. Finally, to resolve the imbalance of the distribution of the various categories of point clouds, the original loss function is optimized by adopting a reweighted method to prevent overfitting so that the network can focus on small target categories in the training process to effectively improve the classification performance. Through the experimental analysis of a Vaihingen (Germany) urban 3D semantic dataset from the ISPRS website, it is verified that the proposed algorithm has a strong generalization ability. The overall accuracy (OA) of the proposed algorithm on the Vaihingen urban 3D semantic dataset reached 97.9%, and the average reached 96.1%. Experiments show that the proposed algorithm fully exploits the semantic features of point clouds and effectively improves the accuracy of point cloud classification.
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Zhang, Hankui K., Dong Luo, and David P. Roy. "Improved Landsat Operational Land Imager (OLI) Cloud and Shadow Detection with the Learning Attention Network Algorithm (LANA)." Remote Sensing 16, no. 8 (April 9, 2024): 1321. http://dx.doi.org/10.3390/rs16081321.
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Landsat cloud and cloud shadow detection has a long heritage based on the application of empirical spectral tests to single image pixels, including the Landsat product Fmask algorithm, which uses spectral tests applied to optical and thermal bands to detect clouds and uses the sun-sensor-cloud geometry to detect shadows. Since the Fmask was developed, convolutional neural network (CNN) algorithms, and in particular U-Net algorithms (a type of CNN with a U-shaped network structure), have been developed and are applied to pixels in square patches to take advantage of both spatial and spectral information. The purpose of this study was to develop and assess a new U-Net algorithm that classifies Landsat 8/9 Operational Land Imager (OLI) pixels with higher accuracy than the Fmask algorithm. The algorithm, termed the Learning Attention Network Algorithm (LANA), is a form of U-Net but with an additional attention mechanism (a type of network structure) that, unlike conventional U-Net, uses more spatial pixel information across each image patch. The LANA was trained using 16,861 512 × 512 30 m pixel annotated Landsat 8 OLI patches extracted from 27 images and 69 image subsets that are publicly available and have been used by others for cloud mask algorithm development and assessment. The annotated data were manually refined to improve the annotation and were supplemented with another four annotated images selected to include clear, completely cloudy, and developed land images. The LANA classifies image pixels as either clear, thin cloud, cloud, or cloud shadow. To evaluate the classification accuracy, five annotated Landsat 8 OLI images (composed of >205 million 30 m pixels) were classified, and the results compared with the Fmask and a publicly available U-Net model (U-Net Wieland). The LANA had a 78% overall classification accuracy considering cloud, thin cloud, cloud shadow, and clear classes. As the LANA, Fmask, and U-Net Wieland algorithms have different class legends, their classification results were harmonized to the same three common classes: cloud, cloud shadow, and clear. Considering these three classes, the LANA had the highest (89%) overall accuracy, followed by Fmask (86%), and then U-Net Wieland (85%). The LANA had the highest F1-scores for cloud (0.92), cloud shadow (0.57), and clear (0.89), and the other two algorithms had lower F1-scores, particularly for cloud (Fmask 0.90, U-Net Wieland 0.88) and cloud shadow (Fmask 0.45, U-Net Wieland 0.52). In addition, a time-series evaluation was undertaken to examine the prevalence of undetected clouds and cloud shadows (i.e., omission errors). The band-specific temporal smoothness index (TSIλ) was applied to a year of Landsat 8 OLI surface reflectance observations after discarding pixel observations labelled as cloud or cloud shadow. This was undertaken independently at each gridded pixel location in four 5000 × 5000 30 m pixel Landsat analysis-ready data (ARD) tiles. The TSIλ results broadly reflected the classification accuracy results and indicated that the LANA had the smallest cloud and cloud shadow omission errors, whereas the Fmask had the greatest cloud omission error and the second greatest cloud shadow omission error. Detailed visual examination, true color image examples and classification results are included and confirm these findings. The TSIλ results also highlight the need for algorithm developers to undertake product quality assessment in addition to accuracy assessment. The LANA model, training and evaluation data, and application codes are publicly available for other researchers.
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Wang, Bo, Mingwei Zhou, Wei Cheng, Yao Chen, Qinghong Sheng, Jun Li, and Li Wang. "An Efficient Cloud Classification Method Based on a Densely Connected Hybrid Convolutional Network for FY-4A." Remote Sensing 15, no. 10 (May 21, 2023): 2673. http://dx.doi.org/10.3390/rs15102673.
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Understanding atmospheric motions and projecting climate changes depends significantly on cloud types, i.e., different cloud types correspond to different atmospheric conditions, and accurate cloud classification can help forecasts and meteorology-related studies to be more effectively directed. However, accurate classification of clouds is challenging and often requires certain manual involvement due to the complex cloud forms and dispersion. To address this challenge, this paper proposes an improved cloud classification method based on a densely connected hybrid convolutional network. A dense connection mechanism is applied to hybrid three-dimensional convolutional neural network (3D-CNN) and two-dimensional convolutional neural network (2D-CNN) architectures to use the feature information of the spatial and spectral channels of the FY-4A satellite fully. By using the proposed network, cloud categorization solutions with a high temporal resolution, extensive coverage, and high accuracy can be obtained without the need for any human intervention. The proposed network is verified using tests, and the results show that it can perform real-time classification tasks for seven different types of clouds and clear skies in the Chinese region. For the CloudSat 2B-CLDCLASS product as a test target, the proposed network can achieve an overall accuracy of 95.2% and a recall of more of than 82.9% for all types of samples, outperforming the other deep-learning-based techniques.
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Lumban-Gaol, Y. A., Z. Chen, M. Smit, X. Li, M. A. Erbaşu, E. Verbree, J. Balado, M. Meijers, and N. van der Vaart. "A COMPARATIVE STUDY OF POINT CLOUDS SEMANTIC SEGMENTATION USING THREE DIFFERENT NEURAL NETWORKS ON THE RAILWAY STATION DATASET." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 223–28. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-223-2021.
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Abstract. Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the point cloud semantic segmentation problem. This paper evaluates three different neural networks for semantic segmentation of point clouds, namely PointNet++, PointCNN and DGCNN. A public indoor scene of the Amersfoort railway station is used as the study area. Unlike the typical indoor scenes and even more from the ubiquitous outdoor ones in currently available datasets, the station consists of objects such as the entrance gates, ticket machines, couches, and garbage cans. For the experiment, we use subsets from the data, remove the noise, evaluate the performance of the selected neural networks. The results indicate an overall accuracy of more than 90% for all the networks but vary in terms of mean class accuracy and mean Intersection over Union (IoU). The misclassification mainly occurs in the classes of couch and garbage can. Several factors that may contribute to the errors are analyzed, such as the quality of the data and the proportion of the number of points per class. The adaptability of the networks is also heavily dependent on the training location: the overall characteristics of the train station make a trained network for one location less suitable for another.
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Zhang, Zheng, Zhiwei Xu, Chang’an Liu, Qing Tian, and Yongsheng Zhou. "Cloudformer V2: Set Prior Prediction and Binary Mask Weighted Network for Cloud Detection." Mathematics 10, no. 15 (July 31, 2022): 2710. http://dx.doi.org/10.3390/math10152710.
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Cloud detection is an essential step in optical remote sensing data processing. With the development of deep learning technology, cloud detection methods have made remarkable progress. Among them, researchers have started to try to introduce Transformer into cloud detection tasks due to its excellent performance in image semantic segmentation tasks. However, the current Transformer-based methods suffer from training difficulty and low detection accuracy of small clouds. To solve these problems, this paper proposes Cloudformer V2 based on the previously proposed Cloudformer. For the training difficulty, Cloudformer V2 uses Set Attention Block to extract intermediate features as Set Prior Prediction to participate in supervision, which enables the model to converge faster. For the detection of small clouds, Cloudformer V2 decodes the features by a multi-scale Transformer decoder, which uses multi-resolution features to improve the modeling accuracy. In addition, a binary mask weighted loss function (BW Loss) is designed to construct weights by counting pixels classified as clouds; thus, guiding the network to focus on features of small clouds and improving the overall detection accuracy. Cloudformer V2 is experimented on the dataset from GF-1 satellite and has excellent performance.
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Chernokulsky, A. V., and A. V. Eliseev. "Climatology of cloud overlap parameter." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 14, no. 1 (2017): 216–25. http://dx.doi.org/10.21046/2070-7401-2017-14-1-216-225.
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Gyasi, Emmanuel Kwabena, and Purushotham Swarnalatha. "Cloud-MobiNet: An Abridged Mobile-Net Convolutional Neural Network Model for Ground-Based Cloud Classification." Atmosphere 14, no. 2 (January 31, 2023): 280. http://dx.doi.org/10.3390/atmos14020280.
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More than 60 percent of the global surface is covered by clouds, and they play a vital role in the hydrological circle, climate change, and radiation budgets by modifying shortwaves and longwave. Weather forecast reports are critical to areas such as air and sea transport, energy, agriculture, and the environment. The time has come for artificial intelligence-powered devices to take the place of the current method by which decision-making experts determine cloud types. Convolutional neural network models (CNNs) are starting to be utilized for identifying the types of clouds that are caused by meteorological occurrences. This study uses the publicly available Cirrus Cumulus Stratus Nimbus (CCSN) dataset, which consists of 2543 ground-based cloud images altogether. We propose a model called Cloud-MobiNet for the classification of ground-based clouds. The model is an abridged convolutional neural network based on MobileNet. The architecture of Cloud-MobiNet is divided into two blocks, namely the MobileNet building block and the support MobileNet block (SM block). The MobileNet building block consists of the weights of the depthwise separable convolutions and pointwise separable convolutions of the MobileNet model. The SM block is made up of three dense network layers for feature extraction. This makes the Cloud-MobiNet model very lightweight to be implemented on a smartphone. An overall accuracy success of 97.45% was obtained for the CCSN dataset used for cloud-type classification. Cloud-MobiNet promises to be a significant model in the short term, since automated ground-based cloud classification is anticipated to be a preferred means of cloud observation, not only in meteorological analysis and forecasting but also in the aeronautical and aviation industries.
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Politz, F., and M. Sester. "EXPLORING ALS AND DIM DATA FOR SEMANTIC SEGMENTATION USING CNNS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 347–54. http://dx.doi.org/10.5194/isprs-archives-xlii-1-347-2018.
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<p><strong>Abstract.</strong> Over the past years, the algorithms for dense image matching (DIM) to obtain point clouds from aerial images improved significantly. Consequently, DIM point clouds are now a good alternative to the established Airborne Laser Scanning (ALS) point clouds for remote sensing applications. In order to derive high-level applications such as digital terrain models or city models, each point within a point cloud must be assigned a class label. Usually, ALS and DIM are labelled with different classifiers due to their varying characteristics. In this work, we explore both point cloud types in a fully convolutional encoder-decoder network, which learns to classify ALS as well as DIM point clouds. As input, we project the point clouds onto a 2D image raster plane and calculate the minimal, average and maximal height values for each raster cell. The network then differentiates between the classes ground, non-ground, building and no data. We test our network in six training setups using only one point cloud type, both point clouds as well as several transfer-learning approaches. We quantitatively and qualitatively compare all results and discuss the advantages and disadvantages of all setups. The best network achieves an overall accuracy of 96<span class="thinspace"></span>% in an ALS and 83<span class="thinspace"></span>% in a DIM test set.</p>
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Lim, Pheng-Un, Chang-Yeol Choi, and Hwang-Kyu Choi. "Cloud Assisted P2P Live Video Streaming over DHT Overlay Network." Transactions of The Korean Institute of Electrical Engineers 66, no. 1 (January 1, 2017): 89–99. http://dx.doi.org/10.5370/kiee.2017.66.1.89.
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Lagahit, M. L. R., Z. Li, K. Sakaguchi, and M. Matsuoka. "EXPLORING GROUND SEGMENTATION FROM LIDAR SCANNING-DERIVED IMAGES USING CONVOLUTIONAL NEURAL NETWORKS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W1-2023 (May 25, 2023): 221–26. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w1-2023-221-2023.
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Abstract. Recent works have attempted to extract features such as road markings from sparse mobile LiDAR scanning point cloud-derived images via convolutional neural networks (CNN). In this paper, the use of such methods for ground segmentation was explored. To begin, point clouds from each channel will be projected onto the y-z plane to generate the images that will be used for training and testing the CNN model. Then, for the main workflow, the following steps were performed for each channel: (1) point cloud-to-image conversion; (2) CNN classification; and (3) image-to-point cloud projection. Then utilizing multi-threading, each channel is processed in parallel to generate our ground-segmented sparse point cloud. Our findings have shown successful ground segmentation, achieving an f1-score of 98.9%. However, it performed 27.81% slower as compared to RANSAC. Overall, this initial investigation has demonstrated that ground segmentation from sparse point cloud-derived imagery is possible, and with further improvements to the CNN model, to make it faster, it has good potential to act as an alternative to conventional point cloud processing.
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Jing, Ran, Fuzhou Duan, Fengxian Lu, Miao Zhang, and Wenji Zhao. "An NDVI Retrieval Method Based on a Double-Attention Recurrent Neural Network for Cloudy Regions." Remote Sensing 14, no. 7 (March 29, 2022): 1632. http://dx.doi.org/10.3390/rs14071632.
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NDVI is an important parameter for environmental assessment and precision agriculture that well-describes the status of vegetation. Nevertheless, the clouds in optical images often result in the absence of NDVI information at key growth stages. The integration of SAR and optical image features will likely address this issue. Although the mapping of different data sources is complex, the prosperity of deep learning technology provides an alternative approach. In this study, the double-attention RNN architecture based on the recurrent neural network (RNN) and attention mechanism is proposed to retrieve NDVI data of cloudy regions. Overall, the NDVI is retrieved by the proposed model from two aspects: the temporal domain and the pixel neighbor domain. The performance of the double-attention RNN is validated through different cloud coverage conditions, input ablation, and comparative experiments with various methods. The results conclude that a high retrieval accuracy is guaranteed by the proposed model, even under high cloud coverage conditions (R2 = 0.856, RMSE = 0.124). Using SAR images independently results in poor NDVI retrieval results (R2 = 0.728, RMSE = 0.141) with considerable artifacts, which need to be addressed with auxiliary data, such as IDM features. Temporal and pixel neighbor features play an important role in improving the accuracy of NDVI retrieval (R2 = 0.894, RMSE = 0.096). For the missing values of NDVI data caused by cloud coverage, the double-attention RNN proposed in this study provides a potential solution for information recovery.
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Jiang, Zhongbin, Hai Tao, and Ye Liu. "Receptive Field Space for Point Cloud Analysis." Sensors 24, no. 13 (July 1, 2024): 4274. http://dx.doi.org/10.3390/s24134274.
Full textAbstract:
Similar to convolutional neural networks for image processing, existing analysis methods for 3D point clouds often require the designation of a local neighborhood to describe the local features of the point cloud. This local neighborhood is typically manually specified, which makes it impossible for the network to dynamically adjust the receptive field’s range. If the range is too large, it tends to overlook local details, and if it is too small, it cannot establish global dependencies. To address this issue, we introduce in this paper a new concept: receptive field space (RFS). With a minor computational cost, we extract features from multiple consecutive receptive field ranges to form this new receptive field space. On this basis, we further propose a receptive field space attention mechanism, enabling the network to adaptively select the most effective receptive field range from RFS, thus equipping the network with the ability to adjust granularity adaptively. Our approach achieved state-of-the-art performance in both point cloud classification, with an overall accuracy (OA) of 94.2%, and part segmentation, achieving an mIoU of 86.0%, demonstrating the effectiveness of our method.
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Gao, Lin, Chenxi Gai, Sijun Lu, and Jinyi Zhang. "MSACN: A Cloud Extraction Method from Satellite Image Using Multiscale Soft Attention Convolutional Neural Network." Applied Sciences 14, no. 8 (April 13, 2024): 3285. http://dx.doi.org/10.3390/app14083285.
Full textAbstract:
In satellite remote sensing images, the existence of clouds has an occlusion effect on ground information. Different degrees of clouds make it difficult for existing models to accurately detect clouds in images due to complex scenes. The detection and extraction of clouds is one of the most important problems to be solved in the further analysis and utilization of image information. In this article, we refined a multi-head soft attention convolutional neural network incorporating spatial information modeling (MSACN). During the encoder process, MSACN extracts cloud features through a concurrent dilated residual convolution module. In the part of the decoder, there is an aggregating feature module that uses a soft attention mechanism. It integrates the semantic information with spatial information to obtain the pixel-level semantic segmentation outputs. To assess the applicability of MSACN, we compare our network with Transform-based and other traditional CNN-based methods on the ZY-3 dataset. Experimental outputs including the other two datasets show that MSACN has a better overall performance for cloud extraction tasks, with an overall accuracy of 98.57%, a precision of 97.61%, a recall of 97.37%, and F1-score of 97.48% and an IOU of 95.10%.
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Cynthia, Eka Pandu, Edi Ismanto, M. Imam Arifandy, S. Sarbaini, N. Nazaruddin, Melda Agnes Manuhutu, Muhammad Ali Akbar, and Abdiyanto. "Convolutional Neural Network and Deep Learning Approach for Image Detection and Identification." Journal of Physics: Conference Series 2394, no. 1 (December 1, 2022): 012019. http://dx.doi.org/10.1088/1742-6596/2394/1/012019.
Full textAbstract:
Abstract There are many different varieties of clouds, each with a unique set of properties. As a result of this variability, it is difficult to discern these sorts of clouds. A database’s objects must be categorized using data categorization in order to be organized into multiple categories. This study made use of the Cirrus Cumulus Stratus Nimbus (CCSN) dataset, which falls under the low cloud category and includes photos of Cumulus (182 images), and Cumulonimbus (242 photographs), and Stratus (242 images) (202 images). A fast R-CNN detector with feature extraction = Resnet50 was used to create a system for classifying cloud kinds. A significant amount of training time is saved by the quicker R-CNN due to its lack of a selective search algorithm. Training loss values for cloud images had an average of 0.9030 from the first epoch through the last one. Using the Faster R-CNN object detection method with the Resnet50 architecture, cloud photos were added and the accuracy was 94.12 and the average precision was 0.76. - Faster R-advantages CNN affect the architecture utilized and are marginally influenced by the algorithm choice, however CNN with Resnet50 is superior overall where these advantages are held.
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Neely, Ryan R., Matthew Hayman, Robert Stillwell, Jeffrey P. Thayer, R. Michael Hardesty, Michael O'Neill, Matthew D. Shupe, and Catherine Alvarez. "Polarization Lidar at Summit, Greenland, for the Detection of Cloud Phase and Particle Orientation." Journal of Atmospheric and Oceanic Technology 30, no. 8 (August 1, 2013): 1635–55. http://dx.doi.org/10.1175/jtech-d-12-00101.1.
Full textAbstract:
Abstract Accurate measurements of cloud properties are necessary to document the full range of cloud conditions and characteristics. The Cloud, Aerosol Polarization and Backscatter Lidar (CAPABL) has been developed to address this need by measuring depolarization, particle orientation, and the backscatter of clouds and aerosols. The lidar is located at Summit, Greenland (72.6°N, 38.5°W; 3200 m MSL), as part of the Integrated Characterization of Energy, Clouds, Atmospheric State, and Precipitation at Summit Project and NOAA's Earth System Research Laboratory's Global Monitoring Division's lidar network. Here, the instrument is described with particular emphasis placed upon the implementation of new polarization methods developed to measure particle orientation and improve the overall accuracy of lidar depolarization measurements. Initial results from the lidar are also shown to demonstrate the ability of the lidar to observe cloud properties.
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Tan, Jing. "Design and Simulation of Cloud Computing Intelligent Routing Algorithms Based on Optical Network." Journal of Nanoelectronics and Optoelectronics 14, no. 12 (December 1, 2019): 1717–24. http://dx.doi.org/10.1166/jno.2019.2698.
Full textAbstract:
In the current communication technology, optical technology has been applied to the network to obtain optical network technology. Among them, optical network technology is optical wavelength division multiplexing (WDM), which can play a larger transmission capacity under lower energy consumption. Further breakthroughs in intelligent optical networks require improvements in routing issues. In this study, firstly, the optical network architecture is analyzed, including wavelength division multiplexing optical network and elastic optical network. Then, the routing problem in optical networks is analyzed, and the main factors affecting the routing problem are extracted. On the basis of studying the energy consumption characteristics of data centers and WDM optical networks, and considering the characteristics of cloud service configuration, evolutionary game theory and optical bypass theory are introduced to obtain an intelligent routing algorithm for cloud computing based on optical networks, and energy consumption tests are carried out on data transmission and processing. In order to reduce the overall energy consumption, the use of IP routers is reduced, and the idle data servers are shut down. Then, it is found that the total energy consumption increases slowly at different times. The energy consumption of evolutionary game theory is compared. Compared with non-evolutionary game theory, the optimized intelligent routing algorithm makes the energy consumption more stable, while reducing the use of servers can further reduce the good expenditure. The proposed algorithm is oriented to optical network, which solves the problem of low overall utilization of network resources and improves the service quality of cloud services.