Journal articles on the topic 'Network extraction'
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1
Neal, Zachary P. "backbone: An R package to extract network backbones." PLOS ONE 17, no. 5 (May 31, 2022): e0269137. http://dx.doi.org/10.1371/journal.pone.0269137.
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Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates a substantially expanded version of the backbone package for R, which now provides methods for extracting backbones from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the backbone package, and future directions for research on network backbone extraction.
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PELLEGRINI, Lilla, Monica LEBA, and Alexandru IOVANOVICI. "CHARACTERIZATION OF URBAN TRANSPORTATION NETWORKS USING NETWORK MOTIFS." Acta Electrotechnica et Informatica 20, no. 4 (January 21, 2020): 3–9. http://dx.doi.org/10.15546/aeei-2020-0019.
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We use tools and techniques specific to the field of complex networks analysis for the identification and extraction of key parameters which define ”good” patterns and practices for designing public transportation networks. Using network motifs we analyze a set of 18 cities using public data sets regarding the topology of network and discuss each of the identified motifs using the concepts and tools of urban planning.
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Baptista, Diego, and Caterina De Bacco. "Principled network extraction from images." Royal Society Open Science 8, no. 7 (July 2021): 210025. http://dx.doi.org/10.1098/rsos.210025.
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Images of natural systems may represent patterns of network-like structure, which could reveal important information about the topological properties of the underlying subject. However, the image itself does not automatically provide a formal definition of a network in terms of sets of nodes and edges. Instead, this information should be suitably extracted from the raw image data. Motivated by this, we present a principled model to extract network topologies from images that is scalable and efficient. We map this goal into solving a routing optimization problem where the solution is a network that minimizes an energy function which can be interpreted in terms of an operational and infrastructural cost. Our method relies on recent results from optimal transport theory and is a principled alternative to standard image-processing techniques that are based on heuristics. We test our model on real images of the retinal vascular system, slime mould and river networks and compare with routines combining image-processing techniques. Results are tested in terms of a similarity measure related to the amount of information preserved in the extraction. We find that our model finds networks from retina vascular network images that are more similar to hand-labelled ones, while also giving high performance in extracting networks from images of rivers and slime mould for which there is no ground truth available. While there is no unique method that fits all the images the best, our approach performs consistently across datasets, its algorithmic implementation is efficient and can be fully automatized to be run on several datasets with little supervision.
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Yang, Kaili, Weihong Cui, Shu Shi, Yu Liu, Yuanjin Li, and Mengyu Ge. "Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images." Applied Sciences 12, no. 9 (May 7, 2022): 4705. http://dx.doi.org/10.3390/app12094705.
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Road network extraction plays a critical role in data updating, urban development, and decision support. To improve the efficiency of labeling road datasets and addressing the problems of traditional methods of manually extracting road networks from high-resolution images, such as their slow speed and heavy workload, this paper proposes a semi-automatic method of road network extraction from high-resolution remote-sensing images. The proposed method needs only a few points to extract a single road in the image. After the roads are extracted one by one, the road network is generated according to the width of each road and the spatial relationships among the roads. For this purpose, we use regional growth, morphology, vector tracking, vector simplification, endpoint modification, road connections, and intersection connections to generate road networks. Experiments on four images with different terrains and different resolutions show that this method has high extraction accuracy under different image conditions. The comparisons with the semi-automatic GVF-snake method based on regional growth also showed its advantages and potentiality. The proposed method is a novel form of semi-automatic road network extraction, and it significantly increases the efficiency of road network extraction.
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HAYASHI, YOICHI. "NEURAL NETWORK RULE EXTRACTION BY A NEW ENSEMBLE CONCEPT AND ITS THEORETICAL AND HISTORICAL BACKGROUND: A REVIEW." International Journal of Computational Intelligence and Applications 12, no. 04 (December 2013): 1340006. http://dx.doi.org/10.1142/s1469026813400063.
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This paper presents theoretical and historical backgrounds related to neural network rule extraction. It also investigates approaches for neural network rule extraction by ensemble concepts. Bologna pointed out that although many authors had generated comprehensive models from individual networks, much less work had been done to explain ensembles of neural networks. This paper carefully surveyed the previous work on rule extraction from neural network ensembles since 1988. We are aware of three major research groups i.e., Bologna' group, Zhou' group and Hayashi' group. The reason of these situations is obvious. Since the structures of previous neural network ensembles were quite complicated, the research on the efficient rule extraction algorithm from neural network ensembles was few although their learning capability was extremely high. Thus, these issues make rule extraction algorithm for neural network ensemble difficult task. However, there is a practical need for new ideas for neural network ensembles in order to realize the extremely high-performance needs of various rule extraction problems in real life. This paper successively explain nature of artificial neural networks, origin of neural network rule extraction, incorporating fuzziness in neural network rule extraction, theoretical foundation of neural network rule extraction, computational complexity of neural network rule extraction, neuro-fuzzy hybridization, previous rule extraction from neural network ensembles and difficulties of previous neural network ensembles. Next, this paper address three principles of proposed neural network rule extraction: to increase recognition rates, to extract rules from neural network ensembles, and to minimize the use of computing resources. We also propose an ensemble-recursive-rule extraction (E-Re-RX) by two or three standard backpropagation to train multi-layer perceptrons (MLPs), which enabled extremely high recognition accuracy and the extraction of comprehensible rules. Furthermore, this enabled rule extraction that resulted in fewer rules than those in previously proposed methods. This paper summarizes experimental results of rule extraction using E-Re-RX by multiple standard backpropagation MLPs and provides deep discussions. The results make it possible for the output from a neural network ensemble to be in the form of rules, thus open the "black box" of trained neural networks ensembles. Finally, we provide valuable conclusions and as future work, three open questions on the E-Re-RX algorithm.
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Et.al, Mahyuddin K. M. Nasution. "Social Network Extraction Unsupervised." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 4443–49. http://dx.doi.org/10.17762/turcomat.v12i3.1824.
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In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.
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Chaitanya, Aravapalli Sri, Suvarna Vani Koneru, and Praveen Kumar Kollu. "Road Network Extraction using Atrous Spatial Pyramid Pooling." International Journal of Innovative Technology and Exploring Engineering 8, no. 9 (July 30, 2019): 31–33. http://dx.doi.org/10.35940/ijitee.h7459.078919.
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Road extraction from satellite images has several Applications such as geographic information system (GIS). Having an accurate and up-to-date road network database will facilitate transportation, disaster management and GPS navigation. Most active field of research for automatic extraction of road network involves semantic segmentation using convolutional neural network (CNN). Although they can produce accurate results, typically the models give up performance for accuracy and vice-versa. In this paper, we are proposing architecture for semantic segmentation of road networks using Atrous Spatial Pyramid Pooling (ASPP). The network contains residual blocks for extracting low level features. Atrous convolutions with different dilation rates are taken and spatial pyramid pooling is performed on these features for extracting the spatial information. The low level features from residual blocks are added to the multi scale context information to produce the final segmentation image. Our proposed model significantly reduces the number of parameters that are required to train the model. The proposed model was trained on the Massachusetts roads dataset and the results have shown that our model produces superior results than that of popular state-of-the art models.
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Luo, Shuai, Kai Yang, Lijuan Yang, Yong Wang, Xiaorong Gao, Tianci Jiang, and Chunjiang Li. "Laser Curve Extraction of Wheelset Based on Deep Learning Skeleton Extraction Network." Sensors 22, no. 3 (January 23, 2022): 859. http://dx.doi.org/10.3390/s22030859.
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In this paper, a new algorithm for extracting the laser fringe center is proposed. Based on a deep learning skeleton extraction network, the laser stripe center can be extracted quickly and accurately. Skeleton extraction is the process of reducing the shape image to its approximate central axis representation while maintaining the image’s topological and geometric shape. Skeleton extraction is an important step in topological and geometric shape analysis. According to the characteristics of the wheelset laser curve dataset, a new skeleton extraction network, a hierarchical skeleton network (LuoNet), is proposed. The proposed architecture has three levels of the encoder–decoder network, and YE Module interconnection is designed between each level of the encoder and decoder network. In the wheelset laser curve dataset, the F1_score can reach 0.714. Compared with the traditional laser curve center extraction algorithm, the proposed LuoNet algorithm has the advantages of short running time, high accuracy, and stable extraction results.
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Milano, Nicola, and Stefano Nolfi. "Autonomous learning of features for control: Experiments with embodied and situated agents." PLOS ONE 16, no. 4 (April 15, 2021): e0250040. http://dx.doi.org/10.1371/journal.pone.0250040.
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The efficacy of evolutionary or reinforcement learning algorithms for continuous control optimization can be enhanced by including an additional neural network dedicated to features extraction trained through self-supervision. In this paper we introduce a method that permits to continue the training of the features extracting network during the training of the control network. We demonstrate that the parallel training of the two networks is crucial in the case of agents that operate on the basis of egocentric observations and that the extraction of features provides an advantage also in problems that do not benefit from dimensionality reduction. Finally, we compare different feature extracting methods and we show that sequence-to-sequence learning outperforms the alternative methods considered in previous studies.
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Hsu, Pai-Hui. "EVALUATING THE INITIALIZATION METHODS OF WAVELET NETWORKS FOR HYPERSPECTRAL IMAGE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 83–89. http://dx.doi.org/10.5194/isprs-archives-xli-b7-83-2016.
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The idea of using artificial neural network has been proven useful for hyperspectral image classification. However, the high dimensionality of hyperspectral images usually leads to the failure of constructing an effective neural network classifier. To improve the performance of neural network classifier, wavelet-based feature extraction algorithms can be applied to extract useful features for hyperspectral image classification. However, the extracted features with fixed position and dilation parameters of the wavelets provide insufficient characteristics of spectrum. In this study, wavelet networks which integrates the advantages of wavelet-based feature extraction and neural networks classification is proposed for hyperspectral image classification. Wavelet networks is a kind of feed-forward neural networks using wavelets as activation function. Both the position and the dilation parameters of the wavelets are optimized as well as the weights of the network during the training phase. The value of wavelet networks lies in their capabilities of optimizing network weights and extracting essential features simultaneously for hyperspectral images classification. In this study, the influence of the learning rate and momentum term during the network training phase is presented, and several initialization modes of wavelet networks were used to test the performance of wavelet networks.
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Hsu, Pai-Hui. "EVALUATING THE INITIALIZATION METHODS OF WAVELET NETWORKS FOR HYPERSPECTRAL IMAGE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 17, 2016): 83–89. http://dx.doi.org/10.5194/isprsarchives-xli-b7-83-2016.
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The idea of using artificial neural network has been proven useful for hyperspectral image classification. However, the high dimensionality of hyperspectral images usually leads to the failure of constructing an effective neural network classifier. To improve the performance of neural network classifier, wavelet-based feature extraction algorithms can be applied to extract useful features for hyperspectral image classification. However, the extracted features with fixed position and dilation parameters of the wavelets provide insufficient characteristics of spectrum. In this study, wavelet networks which integrates the advantages of wavelet-based feature extraction and neural networks classification is proposed for hyperspectral image classification. Wavelet networks is a kind of feed-forward neural networks using wavelets as activation function. Both the position and the dilation parameters of the wavelets are optimized as well as the weights of the network during the training phase. The value of wavelet networks lies in their capabilities of optimizing network weights and extracting essential features simultaneously for hyperspectral images classification. In this study, the influence of the learning rate and momentum term during the network training phase is presented, and several initialization modes of wavelet networks were used to test the performance of wavelet networks.
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Fang, Fang, Kaishun Wu, Yuanyuan Liu, Shengwen Li, Bo Wan, Yanling Chen, and Daoyuan Zheng. "A Coarse-to-Fine Contour Optimization Network for Extracting Building Instances from High-Resolution Remote Sensing Imagery." Remote Sensing 13, no. 19 (September 23, 2021): 3814. http://dx.doi.org/10.3390/rs13193814.
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Building instances extraction is an essential task for surveying and mapping. Challenges still exist in extracting building instances from high-resolution remote sensing imagery mainly because of complex structures, variety of scales, and interconnected buildings. This study proposes a coarse-to-fine contour optimization network to improve the performance of building instance extraction. Specifically, the network contains two special sub-networks: attention-based feature pyramid sub-network (AFPN) and coarse-to-fine contour sub-network. The former sub-network introduces channel attention into each layer of the original feature pyramid network (FPN) to improve the identification of small buildings, and the latter is designed to accurately extract building contours via two cascaded contour optimization learning. Furthermore, the whole network is jointly optimized by multiple losses, that is, a contour loss, a classification loss, a box regression loss and a general mask loss. Experimental results on three challenging building extraction datasets demonstrated that the proposed method outperformed the state-of-the-art methods’ accuracy and quality of building contours.
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Malang, Kanokwan, Shuliang Wang, Yuanyuan Lv, and Aniwat Phaphuangwittayakul. "Skeleton Network Extraction and Analysis on Bicycle Sharing Networks." International Journal of Data Warehousing and Mining 16, no. 3 (July 2020): 146–67. http://dx.doi.org/10.4018/ijdwm.2020070108.
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Skeleton network extraction has been adopted unevenly in transportation networks whose nodes are always represented as spatial units. In this article, the TPks skeleton network extraction method is proposed and applied to bicycle sharing networks. The method aims to reduce the network size while preserving key topologies and spatial features. The authors quantified the importance of nodes by an improved topology potential algorithm. The spatial clustering allows to detect high traffic concentrations and allocate the nodes of each cluster according to their spatial distribution. Then, the skeleton network is constructed by aggregating the most important indicated skeleton nodes. The authors examine the skeleton network characteristics and different spatial information using the original networks as a benchmark. The results show that the skeleton networks can preserve the topological and spatial information similar to the original networks while reducing their size and complexity.
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Lai, Qinghan, Zihan Zhou, and Song Liu. "Joint Entity-Relation Extraction via Improved Graph Attention Networks." Symmetry 12, no. 10 (October 21, 2020): 1746. http://dx.doi.org/10.3390/sym12101746.
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Joint named entity recognition and relation extraction is an essential natural language processing task that aims to identify entities and extract the corresponding relations in an end-to-end manner. At present, compared with the named entity recognition task, the relation extraction task performs poorly on complex text. To solve this problem, we proposed a novel joint model named extracting Entity-Relations viaImproved Graph Attention networks (ERIGAT), which enhances the ability of the relation extraction task. In our proposed model, we introduced the graph attention network to extract entities and relations after graph embedding based on constructing symmetry relations. To mitigate the over-smoothing problem of graph convolutional networks, inspired by matrix factorization, we improved the graph attention network by designing a new multi-head attention mechanism and sharing attention parameters. To enhance the model robustness, we adopted the adversarial training to generate adversarial samples for training by adding tiny perturbations. Comparing with typical baseline models, we comprehensively evaluated our model by conducting experiments on an open domain dataset (CoNLL04) and a medical domain dataset (ADE). The experimental results demonstrate the effectiveness of ERIGAT in extracting entity and relation information.
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WATTS, MICHAEL J. "FUZZY RULE EXTRACTION FROM SIMPLE EVOLVING CONNECTIONIST SYSTEMS." International Journal of Computational Intelligence and Applications 04, no. 03 (September 2004): 299–308. http://dx.doi.org/10.1142/s146902680400132x.
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A method for extracting Zadeh–Mamdani fuzzy rules from a minimalist constructive neural network model is described. The network contains no embedded fuzzy logic elements. The rule extraction algorithm needs no modification of the neural network architecture. No modification of the network learning algorithm is required, nor is it necessary to retain any training examples. The algorithm is illustrated on two well known benchmark data sets and compared with a relevant existing rule extraction algorithm.
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Hedayati, Ali, and S. M. Ghoreishi. "Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modeling of Supercritical CO2 Extraction of Glycyrrhizic Acid from Glycyrrhiza glabra L." Chemical Product and Process Modeling 11, no. 3 (September 1, 2016): 217–30. http://dx.doi.org/10.1515/cppm-2015-0048.
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Abstract In this study, the extraction of Glycyrrhizic acid (GA) from Glycyrrhiza glabra (licorice) root was investigated by Soxhlet extraction and modified supercritical CO2 with water as co-solvents and 30 min of static extraction time. The high performance liquid chromatography (HPLC) was used to identify and quantitatively determine the amount of extracted GA recovery of supercritical CO2 extraction of GA. The extraction recovery was modeled by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Different ANFIS networks (by changing the type of membership functions) were compared with evaluation of networks accuracy in GA recovery prediction and subsequently the suitable network was determined. A three-layer artificial neural network was also developed for modeling of GA extraction from licorice plant root. In this regard, different networks (by changing the number of neurons in the hidden layer and algorithm of network training) were compared with evaluation of networks accuracy in extraction recovery prediction. One-step secant back propagation algorithm with six neurons in hidden layer was found to be the most suitable network and the coefficient of determination (R2) was 98.5 %. Gaussian combination membership function (gauss2mf) using 2 membership function to each input was obtained to be optimum ANFIS architecture with mean square error (MSE) of 0.05,0.17 and 0.07 for training, testing and checking data, respectively.
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S, Santhosh Kumar, Vishnu Vardhan S, Wasim Jaffar M, Sultan Saleem A, and Sharmasth Vali Y. "Social Communicative Extraction Analysis." International Research Journal of Multidisciplinary Technovation 2, no. 4 (September 26, 2020): 4–10. http://dx.doi.org/10.34256/irjmt2042.
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The distinguishing proof of online networking networks has as of late been of significant worry, since clients taking an interest in such networks can add to viral showcasing efforts. Right now center around clients' correspondence considering character as a key trademark for recognizing informative systems for example systems with high data streams. We portray the Twitter Personality based Communicative Communities Extraction (T-PCCE) framework that recognizes the most informative networks in a Twitter organize chart thinking about clients' character. We at that point grow existing methodologies as a part of client’s character extraction by collecting information that speak to a few parts of client conduct utilizing AI strategies. We utilize a current measured quality based network discovery calculation and we expand it by embeddings a post-preparing step that dispenses with diagram edges dependent on clients' character. The adequacy of our methodology is exhibited by testing the Twitter diagram and looking at the correspondence quality of the removed networks with and without considering the character factor. We characterize a few measurements to tally the quality of correspondence inside every network. Our algorithmic system and the resulting usage utilize the cloud foundation and utilize the MapReduce Programming Environment. Our outcomes show that the T-PCCE framework makes the most informative networks.
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Zeng, Lang, Zhen Jia, and Yingying Wang. "Extraction algorithm for optimal coarse-grained networks on complex networks." International Journal of Modern Physics C 30, no. 11 (November 2019): 1950081. http://dx.doi.org/10.1142/s0129183119500815.
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Coarse-graining of complex networks is a hot topic in network science. Coarse-grained networks are required to preserve the topological information or dynamic properties of the original network. Some effective coarse-graining methods have been proposed, while an urgent problem is how to obtain coarse-grained network with optimal scale. In this paper, we propose an extraction algorithm (EA) for optimal coarse-grained networks. Numerical simulation for EA on four kinds of networks and performing Kuramoto model on optimal coarse-grained networks, we find our algorithm can effectively obtain the optimal coarse-grained network.
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Li, Yuxia, Bo Peng, Lei He, Kunlong Fan, Zhenxu Li, and Ling Tong. "Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks." Sensors 19, no. 19 (September 23, 2019): 4115. http://dx.doi.org/10.3390/s19194115.
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Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.
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Wang, Ziyan. "Feature Extraction and Identification of Calligraphy Style Based on Dual Channel Convolution Network." Security and Communication Networks 2022 (May 16, 2022): 1–11. http://dx.doi.org/10.1155/2022/4187797.
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To improve the effect of calligraphy style feature extraction and identification, this study proposes a calligraphy style feature extraction and identification technology based on two-channel convolutional neural network and constructs an intelligent calligraphy style feature extraction and identification system. Moreover, this paper improves the C3D network model and retains 2 fully connected layers. In addition, by extracting the outline skeleton and stroke features of calligraphy characters, this paper calculates the feature weight and authenticity determination function and constructs an authenticity identification system. The experimental study shows that the calligraphy style feature extraction and identification system based on the dual-channel convolutional neural network proposed in this paper has a good performance in calligraphy style feature extraction and identification.
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Qiu, Yue, Fang Wu, Jichong Yin, Chengyi Liu, Xianyong Gong, and Andong Wang. "MSL-Net: An Efficient Network for Building Extraction from Aerial Imagery." Remote Sensing 14, no. 16 (August 12, 2022): 3914. http://dx.doi.org/10.3390/rs14163914.
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There remains several challenges that are encountered in the task of extracting buildings from aerial imagery using convolutional neural networks (CNNs). First, the tremendous complexity of existing building extraction networks impedes their practical application. In addition, it is arduous for networks to sufficiently utilize the various building features in different images. To address these challenges, we propose an efficient network called MSL-Net that focuses on both multiscale building features and multilevel image features. First, we use depthwise separable convolution (DSC) to significantly reduce the network complexity, and then we embed a group normalization (GN) layer in the inverted residual structure to alleviate network performance degradation. Furthermore, we extract multiscale building features through an atrous spatial pyramid pooling (ASPP) module and apply long skip connections to establish long-distance dependence to fuse features at different levels of the given image. Finally, we add a deformable convolution network layer before the pixel classification step to enhance the feature extraction capability of MSL-Net for buildings with irregular shapes. The experimental results obtained on three publicly available datasets demonstrate that our proposed method achieves state-of-the-art accuracy with a faster inference speed than that of competing approaches. Specifically, the proposed MSL-Net achieves 90.4%, 81.1% and 70.9% intersection over union (IoU) values on the WHU Building Aerial Imagery dataset, Inria Aerial Image Labeling dataset and Massachusetts Buildings dataset, respectively, with an inference speed of 101.4 frames per second (FPS) for an input image of size 3 × 512 × 512 on an NVIDIA RTX 3090 GPU. With an excellent tradeoff between accuracy and speed, our proposed MSL-Net may hold great promise for use in building extraction tasks.
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Lin, Bin, Houcheng Su, Danyang Li, Ao Feng, Hongxiang Li, Jiao Li, Kailin Jiang, Hongbo Jiang, Xinyao Gong, and Tao Liu. "PlaneNet: an efficient local feature extraction network." PeerJ Computer Science 7 (December 7, 2021): e783. http://dx.doi.org/10.7717/peerj-cs.783.
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Due to memory and computing resources limitations, deploying convolutional neural networks on embedded and mobile devices is challenging. However, the redundant use of the 1 × 1 convolution in traditional light-weight networks, such as MobileNetV1, has increased the computing time. By utilizing the 1 × 1 convolution that plays a vital role in extracting local features more effectively, a new lightweight network, named PlaneNet, is introduced. PlaneNet can improve the accuracy and reduce the numbers of parameters and multiply-accumulate operations (Madds). Our model is evaluated on classification and semantic segmentation tasks. In the classification tasks, the CIFAR-10, Caltech-101, and ImageNet2012 datasets are used. In the semantic segmentation task, PlaneNet is tested on the VOC2012 datasets. The experimental results demonstrate that PlaneNet (74.48%) can obtain higher accuracy than MobileNetV3-Large (73.99%) and GhostNet (72.87%) and achieves state-of-the-art performance with fewer network parameters in both tasks. In addition, compared with the existing models, it has reached the practical application level on mobile devices. The code of PlaneNet on GitHub: https://github.com/LinB203/planenet.
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Son. "AUTOMATIC KEYWORD EXTRACTION USING ARTIFICIAL NEURAL NETWORK AND FEATURE EXTRACTION." Journal of Military Science and Technology, no. 69A (November 16, 2020): 63–74. http://dx.doi.org/10.54939/1859-1043.j.mst.69a.2020.63-74.
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Extracting keywords from documents is an essential task in natural language processing. A challenge of this task is to define a reasonable set of keywords from which we can find all relevant documents. This paper proposes a new approach that exploits word-level handcrafted features and machine learning models to select a single document's most important keywords. To evaluate the proposed solution, we compare our results with the latest supervised and unsupervised automatic keyword extraction methods. Experiment results show that our model achieves the best results on the 9/20 data corpus. It points out that our proposed approach is promising.
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Li, Xiang, Junan Yang, Pengjiang Hu, and Hui Liu. "LAPREL: A Label-Aware Parallel Network for Relation Extraction." Symmetry 13, no. 6 (May 28, 2021): 961. http://dx.doi.org/10.3390/sym13060961.
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Relation extraction is a crucial task in natural language processing (NLP) that aims to extract all relational triples from a given sentence. Extracting overlapping relational triples from complex texts is challenging and has received extensive research attention. Most existing methods are based on cascade models and employ language models to transform the given sentence into vectorized representations. The cascaded structure can cause exposure bias issue; however, the vectorized representation of each sentence needs to be closely related to the relation extraction with pre-defined relation types. In this paper, we propose a label-aware parallel network (LAPREL) for relation extraction. To solve the exposure bias issue, we apply a parallel network, instead of the cascade framework, based on the table-filling method with a symmetric relation pair tagger. To obtain task-related sentence embedding, we embed the prior label information into the token embedding and adjust the sentence embedding for each relation type. The proposed method can also effectively deal with overlapping relational triples. Compared with 10 baselines, extensive experiments are conducted on two public datasets to verify the performance of our proposed network. The experimental results show that LAPREL outperforms the 10 baselines in extracting relational triples from complex text.
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Yi, Cheng. "Application of Convolutional Networks in Clothing Design from the Perspective of Deep Learning." Scientific Programming 2022 (September 27, 2022): 1–8. http://dx.doi.org/10.1155/2022/6173981.
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A convolutional neural network (CNN) is a machine learning method under supervised learning. It not only has the advantages of high fault tolerance and self-learning ability of other traditional neural networks but also has the advantages of weight sharing, automatic feature extraction, and the combination of the input image and network. It avoids the process of data reconstruction and feature extraction in traditional recognition algorithms. For example, as an unsupervised generation model, the convolutional confidence network (CCN) generated by the combination of convolutional neural network and confidence network has been successfully applied to face feature extraction.
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MCGARRY, KENNETH, STEFAN WERMTER, and JOHN MACINTYRE. "THE EXTRACTION AND COMPARISON OF KNOWLEDGE FROM LOCAL FUNCTION NETWORKS." International Journal of Computational Intelligence and Applications 01, no. 04 (December 2001): 369–82. http://dx.doi.org/10.1142/s1469026801000305.
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Extracting rules from RBFs is not a trivial task because of nonlinear functions or high input dimensionality. In such cases, some of the hidden units of the RBF network have a tendency to be "shared" across several output classes or even may not contribute to any output class. To address this we have developed an algorithm called LREX (for Local Rule EXtraction) which tackles these issues by extracting rules at two levels: hREX extracts rules by examining the hidden unit to class assignments while mREX extracts rules based on the input space to output space mappings. The rules extracted by our algorithm are compared and contrasted against a competing local rule extraction system. The central claim of this paper is that local function networks such as radial basis function (RBF) networks have a suitable architecture based on Gaussian functions that is amenable to rule extraction.
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Liu, Yangtian, Xiaopeng Yan, Xinhong Hao, Guanghua Yi, and Dingkun Huang. "Automatic Modulation Recognition of Radiation Source Signals Based on Data Rearrangement and the 2D FFT." Remote Sensing 15, no. 2 (January 15, 2023): 518. http://dx.doi.org/10.3390/rs15020518.
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It is a challenge for automatic modulation recognition (AMR) methods for radiation source signals to work in environments with low signal-to-noise ratios (SNRs). This paper proposes a modulation feature extraction method based on data rearrangement and the 2D fast Fourier transform (FFT) (DR2D), and a DenseNet feature extraction network with early fusion is constructed to recognize the extracted modulation features. First, the input signal is preprocessed by DR2D to obtain three types of joint frequency feature bins with multiple time scales. Second, the feature fusion operation is performed on the inputs of the different layers of the proposed network. Finally, feature recognition is completed in the subsequent layers. The theoretical analysis and simulation results show that DR2D is a fast and robust preprocessing method for extracting the features of modulated radiation source signals with less computational complexity. The proposed DenseNet feature extraction network with early fusion can identify the extracted modulation features with less spatial complexity than other types of convolutional neural networks (CNNs) and performs well in low-SNR environments.
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de-la-Bandera, Isabel, David Palacios, Jessica Mendoza, and Raquel Barco. "Feature Extraction for Dimensionality Reduction in Cellular Networks Performance Analysis." Sensors 20, no. 23 (December 4, 2020): 6944. http://dx.doi.org/10.3390/s20236944.
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Next-generation mobile communications networks will have to cope with an extraordinary amount and variety of network performance indicators, causing an increase in the storage needs of the network databases and the degradation of the management functions due to the high-dimensionality of every network observation. In this paper, different techniques for feature extraction are described and proposed as a means for reducing this high dimensionality, to be integrated as an intermediate stage between the monitoring of the network performance indicators and their usage in mobile networks’ management functions. Results using a dataset gathered from a live cellular network show the benefits of this approach, in terms both of storage savings and subsequent management function improvements.
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K.K, Dr Arun. "Tumor Classification and Extraction from Mammogram Images Using Convolutional Neural Network." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 2636–41. http://dx.doi.org/10.5373/jardcs/v12sp7/20202400.
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Liu, Congcong, Jiangyong He, Pan Wang, Dengke Xing, Jin Li, Yange Liu, and Zhi Wang. "Characteristic extraction of soliton dynamics based on convolutional autoencoder neural network." Chinese Optics Letters 21, no. 3 (2023): 031901. http://dx.doi.org/10.3788/col202321.031901.
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Zhou, H., Y. Ren, Q. Li, J. Yin, and Y. Lin. "MASNET: IMPROVE PERFORMANCE OF SIAMESE NETWORKS WITH MUTUAL-ATTENTION FOR REMOTE SENSING CHANGE DETECTION TASKS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 681–87. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-681-2022.
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Abstract. Siamese networks are widely used for remote sensing change detection tasks. A vanilla siamese network has two identical feature extraction branches which share weights, these two branches work independently and the feature maps are not fused until about to be sent to a decoder head. However we find that it is critical to exchange information between two feature extraction branches at early stage for change detection task. In this work we present Mutual-Attention Siamese Network (MASNet), a general siamese network with mutual-attention plug-in, so to exchange information between the two feature extraction branches. We show that our modification improve the performance of siamese networks on multi change detection datasets, and it works for both convolutional neural network and visual transformer.
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Stephenson, Scott R., and John A. Agnew. "The work of networks: Embedding firms, transport, and the state in the Russian Arctic oil and gas sector." Environment and Planning A: Economy and Space 48, no. 3 (November 26, 2015): 558–76. http://dx.doi.org/10.1177/0308518x15617755.
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The “network” has gained widespread acceptance within economic geography as a metaphor for economic interaction. Consistent with a global production network (GPN) approach, extractive industries are deeply embedded in political structures, physical infrastructure, and environmental conditions. We advocate for a GPN framework that emphasizes the co-operation of multiple, differentiated networks at each stage of a production network. Furthermore, the physical geography of sub-national spaces as well as trans-national spaces linking resources with destination markets imposes critical constraints on the structure and operation of oil and natural-gas extraction. We attempt to move beyond notions of a singular network encompassing all aspects of production by contextualizing extractive activities within the geopolitical economy of Arctic Russia. Our aim is twofold: to develop a more carefully articulated conception of networks based on the different economic principles and political regulation at work within different types of networks, and to show how the Russian Arctic oil and gas sector can only be adequately understood with such a nuanced approach. The Arctic case illustrates well the complex entanglement of the state and political actors in networks of firms and specialized transport systems. We first deconstruct the network concept to establish the economic principles, actors, and spaces that comprise the extractive production network, and then examine the extractive hydrocarbon networks active in Arctic Russia through this analytical lens.
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Fu, Qiang, and Hongbin Dong. "Breast Cancer Recognition Using Saliency-Based Spiking Neural Network." Wireless Communications and Mobile Computing 2022 (March 24, 2022): 1–17. http://dx.doi.org/10.1155/2022/8369368.
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The spiking neural networks (SNNs) use event-driven signals to encode physical information for neural computation. SNN takes the spiking neuron as the basic unit. It modulates the process of nerve cells from receiving stimuli to firing spikes. Therefore, SNN is more biologically plausible. Although the SNN has more characteristics of biological neurons, SNN is rarely used for medical image recognition due to its poor performance. In this paper, a reservoir spiking neural network is used for breast cancer image recognition. Due to the difficulties of extracting the lesion features in medical images, a salient feature extraction method is used in image recognition. The salient feature extraction network is composed of spiking convolution layers, which can effectively extract the features of lesions. Two temporal encoding manners, namely, linear time encoding and entropy-based time encoding methods, are used to encode the input patterns. Readout neurons use the ReSuMe algorithm for training, and the Fruit Fly Optimization Algorithm (FOA) is employed to optimize the network architecture to further improve the reservoir SNN performance. Three modality datasets are used to verify the effectiveness of the proposed method. The results show an accuracy of 97.44% for the BreastMNIST database. The classification accuracy is 98.27% on the mini-MIAS database. And the overall accuracy is 95.83% for the BreaKHis database by using the saliency feature extraction, entropy-based time encoding, and network optimization.
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Luo, Yuanjiang, Ao Feng, Hongxiang Li, Danyang Li, Xuan Wu, Jie Liao, Chengwu Zhang, Xingqiang Zheng, and Haibo Pu. "New deep learning method for efficient extraction of small water from remote sensing images." PLOS ONE 17, no. 8 (August 5, 2022): e0272317. http://dx.doi.org/10.1371/journal.pone.0272317.
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Extracting water bodies from remote sensing images is important in many fields, such as in water resources information acquisition and analysis. Conventional methods of water body extraction enhance the differences between water bodies and other interfering water bodies to improve the accuracy of water body boundary extraction. Multiple methods must be used alternately to extract water body boundaries more accurately. Water body extraction methods combined with neural networks struggle to improve the extraction accuracy of fine water bodies while ensuring an overall extraction effect. In this study, false color processing and a generative adversarial network (GAN) were added to reconstruct remote sensing images and enhance the features of tiny water bodies. In addition, a multi-scale input strategy was designed to reduce the training cost. We input the processed data into a new water body extraction method based on strip pooling for remote sensing images, which is an improvement of DeepLabv3+. Strip pooling was introduced in the DeepLabv3+ network to better extract water bodies with a discrete distribution at long distances using different strip kernels. The experiments and tests show that the proposed method can improve the accuracy of water body extraction and is effective in fine water body extraction. Compared with seven other traditional remote sensing water body extraction methods and deep learning semantic segmentation methods, the prediction accuracy of the proposed method reaches 94.72%. In summary, the proposed method performs water body extraction better than existing methods.
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Zhang, HuiHui, Hugo A. Loáiciga, LuWei Feng, Jing He, and QingYun Du. "Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models." ISPRS International Journal of Geo-Information 10, no. 3 (March 21, 2021): 186. http://dx.doi.org/10.3390/ijgi10030186.
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Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.
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Zhang, Wenzhuo, Mingyang Yu, Xiaoxian Chen, Fangliang Zhou, Jie Ren, Haiqing Xu, and Shuai Xu. "Combining Deep Fully Convolutional Network and Graph Convolutional Neural Network for the Extraction of Buildings from Aerial Images." Buildings 12, no. 12 (December 15, 2022): 2233. http://dx.doi.org/10.3390/buildings12122233.
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Deep learning technology, such as fully convolutional networks (FCNs), have shown competitive performance in the automatic extraction of buildings from high-resolution aerial images (HRAIs). However, there are problems of over-segmentation and internal cavity in traditional FCNs used for building extraction. To address these issues, this paper proposes a new building graph convolutional network (BGC-Net), which optimizes the segmentation results by introducing the graph convolutional network (GCN). The core of BGC-Net includes two major modules. One is an atrous attention pyramid (AAP) module, obtained by fusing the attention mechanism and atrous convolution, which improves the performance of the model in extracting multi-scale buildings through multi-scale feature fusion; the other is a dual graph convolutional (DGN) module, the build of which is based on GCN, which improves the segmentation accuracy of object edges by adding long-range contextual information. The performance of BGC-Net is tested on two high spatial resolution datasets (Wuhan University building dataset and a Chinese typical city building dataset) and compared with several state-of-the-art networks. Experimental results demonstrate that the proposed method outperforms several state-of-the-art approaches (FCN8s, DANet, SegNet, U-Net, ARC-Net, BAR-Net) in both visual interpretation and quantitative evaluations. The BGC-Net proposed in this paper has better results when extracting the completeness of buildings, including boundary segmentation accuracy, and shows great potential in high-precision remote sensing mapping applications.
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Mosafi, Itay, Eli (Omid) David, Yaniv Altshuler, and Nathan S. Netanyahu. "DNN Intellectual Property Extraction Using Composite Data." Entropy 24, no. 3 (February 28, 2022): 349. http://dx.doi.org/10.3390/e24030349.
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As state-of-the-art deep neural networks are being deployed at the core level of increasingly large numbers of AI-based products and services, the incentive for “copying them” (i.e., their intellectual property, manifested through the knowledge that is encapsulated in them) either by adversaries or commercial competitors is expected to considerably increase over time. The most efficient way to extract or steal knowledge from such networks is by querying them using a large dataset of random samples and recording their output, which is followed by the training of a student network, aiming to eventually mimic these outputs, without making any assumption about the original networks. The most effective way to protect against such a mimicking attack is to answer queries with the classification result only, omitting confidence values associated with the softmax layer. In this paper, we present a novel method for generating composite images for attacking a mentor neural network using a student model. Our method assumes no information regarding the mentor’s training dataset, architecture, or weights. Furthermore, assuming no information regarding the mentor’s softmax output values, our method successfully mimics the given neural network and is capable of stealing large portions (and sometimes all) of its encapsulated knowledge. Our student model achieved 99% relative accuracy to the protected mentor model on the Cifar-10 test set. In addition, we demonstrate that our student network (which copies the mentor) is impervious to watermarking protection methods and thus would evade being detected as a stolen model by existing dedicated techniques. Our results imply that all current neural networks are vulnerable to mimicking attacks, even if they do not divulge anything but the most basic required output, and that the student model that mimics them cannot be easily detected using currently available techniques.
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Craven, Mark W., and Jude W. Shavlik. "Understanding Time-Series Networks: A Case Study in Rule Extraction." International Journal of Neural Systems 08, no. 04 (August 1997): 373–84. http://dx.doi.org/10.1142/s0129065797000380.
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A significant limitation of neural networks is that the representation they learn are usually incomprehensible to humans. We have developed an algorithm, called TREPAN, for extracting comprehensible, symbolic representations from trained neural networks. Given a trained network, TREPAN produces a decision tree that approximates the concept represented by the network. In this article, we discuss the application of TREPAN to a neural network trained on a noisy time series task: predicting the Dollar–Mark exchange rate. We present experiments that show that TREPAN is able to extract a decision tree from this network that equals the network in terms of predictive accuracy, yet provides a comprehensible concept representation. Moreover, our experiments indicate that decision trees induced directly from the training data using conventional algorithms do not match the accuracy nor the comprehensibility of the tree extracted by TREPAN.
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Wang, Xiangqian, Fang Huang, Wencong Wan, and Chengyuan Zhang. "Academic Activities Transaction Extraction Based on Deep Belief Network." Advances in Multimedia 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/5067069.
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Extracting information about academic activity transactions from unstructured documents is a key problem in the analysis of academic behaviors of researchers. The academic activities transaction includes five elements: person, activities, objects, attributes, and time phrases. The traditional method of information extraction is to extract shallow text features and then to recognize advanced features from text with supervision. Since the information processing of different levels is completed in steps, the error generated from various steps will be accumulated and affect the accuracy of final results. However, because Deep Belief Network (DBN) model has the ability to automatically unsupervise learning of the advanced features from shallow text features, the model is employed to extract the academic activities transaction. In addition, we use character-based feature to describe the raw features of named entities of academic activity, so as to improve the accuracy of named entity recognition. In this paper, the accuracy of the academic activities extraction is compared by using character-based feature vector and word-based feature vector to express the text features, respectively, and with the traditional text information extraction based on Conditional Random Fields. The results show that DBN model is more effective for the extraction of academic activities transaction information.
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Kumar, M., R. K. Singh, P. L. N. Raju, and Y. V. N. Krishnamurthy. "Road Network Extraction from High Resolution Multispectral Satellite Imagery Based on Object Oriented Techniques." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 107–10. http://dx.doi.org/10.5194/isprsannals-ii-8-107-2014.
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High Resolution satellite Imagery is an important source for road network extraction for urban road database creation, refinement and updating. However due to complexity of the scene in an urban environment, automated extraction of such features using various line and edge detection algorithms is limited. In this paper we present an integrated approach to extract road network from high resolution space imagery. The proposed approach begins with segmentation of the scene with Multi-resolution Object Oriented segmentation. This step focuses on exploiting both spatial and spectral information for the target feature extraction. The road regions are automatically identified using a soft fuzzy classifier based on a set of predefined membership functions. A number of shape descriptors are computed to reduce the misclassifications between road and other spectrally similar objects. The detected road segments are further refined using morphological operations to form final road network, which is then evaluated for its completeness, correctness and quality. The experiments were carried out of fused IKONOS 2 , Quick bird ,Worldview 2 Products with fused resolution’s ranging from 0.5m to 1 m. Results indicate that the proposed methodology is effective in extracting accurate road networks from high resolution imagery.
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Huan, Hai, Yu Sheng, Yi Zhang, and Yuan Liu. "Strip Attention Networks for Road Extraction." Remote Sensing 14, no. 18 (September 9, 2022): 4516. http://dx.doi.org/10.3390/rs14184516.
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In recent years, deep learning methods have been widely used for road extraction in remote sensing images. However, the existing deep learning semantic segmentation networks generally show poor continuity in road segmentation due to the high-class similarity between roads and buildings surrounding roads in remote sensing images, and the existence of shadows and occlusion. To deal with this problem, this paper proposes strip attention networks (SANet) for extracting roads in remote sensing images. Firstly, a strip attention module (SAM) is designed to extract the contextual information and spatial position information of the roads. Secondly, a channel attention fusion module (CAF) is designed to fuse low-level features and high-level features. The network is trained and tested using the CITY-OSM dataset, DeepGlobe road extraction dataset, and CHN6-CUG dataset. The test results indicate that SANet exhibits excellent road segmentation performance and can better solve the problem of poor road segmentation continuity compared with other networks.
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D’ALCHÉ-BUC, FLORENCE, VINCENT ANDRÈS, and JEAN-PIERRE NADAL. "RULE EXTRACTION WITH FUZZY NEURAL NETWORK." International Journal of Neural Systems 05, no. 01 (March 1994): 1–11. http://dx.doi.org/10.1142/s0129065794000025.
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This paper deals with the learning of understandable decision rules with connectionist systems. Our approach consists of extracting fuzzy control rules with a new fuzzy neural network. Whereas many other works on this area propose to use combinations of nonlinear neurons to approximate fuzzy operations, we use a fuzzy neuron that computes max-min operations. Thus, this neuron can be interpreted as a possibility estimator, just as sigma-pi neurons can support a probabilistic interpretation. Within this context, possibilistic inferences can be drawn through the multi-layered network, using a distributed representation of the information. A new learning procedure has been developed in order that each part of the network can be learnt sequentially, while other parts are frozen. Each step of the procedure is based on the same kind of learning scheme: the backpropagation of a well-chosen cost function with appropriate derivatives of max-min function. An appealing result of the learning phase is the ability of the network to automatically reduce the number of the condition-parts of the rules, if needed. The network has been successfully tested on the learning of a control rule base for an inverted pendulum.
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Nasution, M. K. M., M. Hardi, and R. Syah. "Mining of the social network extraction." Journal of Physics: Conference Series 801 (January 2017): 012020. http://dx.doi.org/10.1088/1742-6596/801/1/012020.
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Grote, Anne, Christian Heipke, and Franz Rottensteiner. "Road Network Extraction in Suburban Areas." Photogrammetric Record 27, no. 137 (March 2012): 8–28. http://dx.doi.org/10.1111/j.1477-9730.2011.00670.x.
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Ahmed, Hasin Afzal, Priyakshi Mahanta, and Dhruba Kumar Bhattacharyya. "Negative Correlation Aided Network Module Extraction." Procedia Technology 6 (2012): 658–65. http://dx.doi.org/10.1016/j.protcy.2012.10.079.
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Park, Seongsik, and Harksoo Kim. "Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence." Applied Sciences 10, no. 11 (June 1, 2020): 3851. http://dx.doi.org/10.3390/app10113851.
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Relation extraction is a type of information extraction task that recognizes semantic relationships between entities in a sentence. Many previous studies have focused on extracting only one semantic relation between two entities in a single sentence. However, multiple entities in a sentence are associated through various relations. To address this issue, we proposed a relation extraction model based on a dual pointer network with a multi-head attention mechanism. The proposed model finds n-to-1 subject–object relations using a forward object decoder. Then, it finds 1-to-n subject–object relations using a backward subject decoder. Our experiments confirmed that the proposed model outperformed previous models, with an F1-score of 80.8% for the ACE (automatic content extraction) 2005 corpus and an F1-score of 78.3% for the NYT (New York Times) corpus.
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O’Brien, Douglas. "Road network extraction from spot panchromatic data." CISM journal 43, no. 2 (July 1989): 121–26. http://dx.doi.org/10.1139/geomat-1989-0011.
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The efficient revision of cartographic data bases using digital imagery implies some form of feature extraction. At present classification techniques can be used to extract certain area outlines, but the automatic detection of linear features such as roads is difficult. One possible method of extracting these features is through mathematical morphology. Mathematical morphology is a form of image treatment that has been applied to remote sensing in recent years. It is possible to extract a binary representation of the road network in an image using this approach, and then treat the results to remove extraneous features. The results demonstrate this process extracts, to a large extent, the information desired. Several caveats must be considered before viewing this as a practical solution. These include processing time, areas where it can be applied, and content of the results.
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Han, Jiapeng, Zhenzhou Wang, Yun Wang, and Weimin Hou. "Building extraction algorithm from remote sensing images based on improved DeepLabv3+ network." Journal of Physics: Conference Series 2303, no. 1 (July 1, 2022): 012010. http://dx.doi.org/10.1088/1742-6596/2303/1/012010.
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Abstract With the development of deep learning, quickly extracting high-precision building information from remote sensing images has become the research focus of intelligent application and processing of remote sensing data. Aiming at the problems of slow extraction speed and incomplete edge segmentation in building extraction in remote sensing images, a building extraction algorithm of remote sensing images based on an improved deeplabv3+ network is proposed. The more lightweight network MobileNetv3 is used to replace the original deeplabv3+ semantic segmentation model feature extraction backbone network Xception, and the standard convolution in the hole space pyramid pooling module is replaced with deep separable convolution, which reduces the amount of calculation and improves the training speed.DAMM (Dual Attention Mechanism Module) is connected in parallel with ASPP (Atous Spatial Pyramid Pooling) to improve the segmentation accuracy of edge targets. The model is verified on WHU and Massachusetts data sets. The results show that the number of training parameters and training time of the model are reduced, and the accuracy of the building extraction is effectively improved, which can meet the requirements of rapid extraction of high-precision buildings.
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Xu, Gang, Min Deng, Geng Sun, Ya Guo, and Jie Chen. "Improving Building Extraction by Using Knowledge Distillation to Reduce the Impact of Label Noise." Remote Sensing 14, no. 22 (November 8, 2022): 5645. http://dx.doi.org/10.3390/rs14225645.
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Building extraction using deep learning techniques has advantages but relies on a large number of clean labeled samples to train the model. Complex appearance and tilt shots often cause many offsets between building labels and true locations, and these noises have a considerable impact on building extraction. This paper proposes a new knowledge distillation-based building extraction method to reduce the impact of noise on the model and maintain the generalization of the model. The method can maximize the generalizable knowledge of large-scale noisy samples and the accurate supervision of small-scale clean samples. The proposed method comprises two similar teacher and student networks, where the teacher network is trained by large-scale noisy samples and the student network is trained by small-scale clean samples and guided by the knowledge of the teacher network. Experimental results show that the student network can not only alleviate the influence of noise labels but also obtain the capability of building extraction without incorrect labels in the teacher network and improve the performance of building extraction.
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Chen, Chen, Yafei Song, Shaohua Yue, Xiaodong Xu, Lihua Zhou, Qibin Lv, and Lintao Yang. "FCNN-SE: An Intrusion Detection Model Based on a Fusion CNN and Stacked Ensemble." Applied Sciences 12, no. 17 (August 27, 2022): 8601. http://dx.doi.org/10.3390/app12178601.
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As a security defense technique to protect networks from attacks, a network intrusion detection model plays a crucial role in the security of computer systems and networks. Aiming at the shortcomings of a complex feature extraction process and insufficient information extraction of the existing intrusion detection models, an intrusion detection model named the FCNN-SE, which uses the fusion convolutional neural network (FCNN) for feature extraction and stacked ensemble (SE) for classification, is proposed in this paper. The proposed model mainly includes two parts, feature extraction and feature classification. Multi-dimensional features of traffic data are first extracted using convolutional neural networks of different dimensions and then fused into a network traffic dataset. The heterogeneous base learners are combined and used as a classifier, and the obtained network traffic dataset is fed to the classifier for final classification. The comprehensive performance of the proposed model is verified through experiments, and experimental results are evaluated using a comprehensive performance evaluation method based on the radar chart method. The comparison results on the NSL-KDD dataset show that the proposed FCNN-SE has the highest overall performance among all compared models, and a more balanced performance than the other models.