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oe1(光电查) - 科学论文

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出版时间
  • 2019
  • 2018
研究主题
  • pattern recognition
  • image
  • partial discharge
  • convolutional neural network(CNN)
  • Conditional Random Fields (CRF)
  • Convolutional Neural Network (CNN)
  • Fine Classification
  • Airborne hyperspectral
  • green tide
  • Elegant End-to-End Fully Convolutional Network (E3FCN)
应用领域
  • Optoelectronic Information Science and Engineering
机构单位
  • Shanghai Jiao Tong University
  • Ocean University of China
  • University of Oulu
  • Wuhan University
  • Central South University
  • Hubei University
300 条数据
?? 中文（中国） ▾

  • Road Segmentation Based on Hybrid Convolutional Network for High-Resolution Visible Remote Sensing Image

    摘要： Road segmentation plays an important role in many applications, such as intelligent transportation system and urban planning. Various road segmentation methods have been proposed for visible remote sensing images, especially the popular convolutional neural network-based methods. However, high-accuracy road segmentation from high-resolution visible remote sensing images is still a challenging problem due to complex background and multiscale roads in these images. To handle this problem, a hybrid convolutional network (HCN), fusing multiple subnetworks, is proposed in this letter. The HCN contains a fully convolutional network, a modi?ed U-Net, and a VGG subnetwork; these subnetworks obtain a coarse-grained, a medium-grained, and a ?ne-grained road segmentation map. Moreover, the HCN uses a shallow convolutional subnetwork to fuse these multigrained segmentation maps for ?nal road segmentation. Bene?tting from multigrained segmentation, our HCN shows impressing results in processing both multiscale roads and complex background. Four testing indicators, including pixel accuracy, mean accuracy, mean region intersection over union (IU), and frequency weighted IU, are computed to evaluate the proposed HCN on two testing data sets. Compared with ?ve state-of-the-art road segmentation methods, our HCN has higher segmentation accuracy than them.

    关键词： high-resolution visible remote sensing image,Convolutional neural network (CNN),road segmentation

    更新于2025-09-23 15:21:21

  • [IEEE 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Beijing (2018.8.19-2018.8.20)] 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Road Map Update from Satellite Images by Object Segmentation and Change Analysis

    摘要： This paper studies to detect the change of road network from remote sensing images. Our purpose is to apply the method for practical usages, such as navigation map updating, road construction supervision, disaster survey, and so on. The proposed approach assumes that there is an outdated road map and the updating job is performed by detecting new road network and comparing the changes. The deep convolution network is utilized for precisely segmenting road areas. An image registration and correction procedure is performed to unify the spatial coordinate reference between the old map and the new road detection results. Then, we modify and standardize the extracted road segments, and apply it to determine the road variation of different periods. Experiments show that, the proposed method successfully identifies road changes, which is useful for fast map update in remote areas.

    关键词： road change detection,road region extraction,image segmentation,deep convolutional networks

    更新于2025-09-23 15:21:21

  • A computer-aided diagnosis system for HEp-2 fluorescence intensity classification

    摘要： Background and objective: The indirect immunofluorescence (IIF) on HEp-2 cells is the recommended technique for the detection of antinuclear antibodies. However, it is burdened by some limitations, as it is time consuming and subjective, and it requires trained personnel. In other fields the adoption of deep neural networks has provided an effective high-level abstraction of the raw data, resulting in the ability to automatically generate optimized high-level features. Methods: To alleviate IIF limitations, this paper presents a computer-aided diagnosis (CAD) system classifying HEp-2 fluorescence intensity: it represents each image using an Invariant Scattering Convolutional Network (Scatnet), which is locally translation invariant and stable to deformations, a characteristic useful in case of HEp-2 samples. To cope with the inter-observer discrepancies found in the dataset, we also introduce a method for gold standard computation that assigns a label and a reliability score to each HEp-2 sample on the basis of annotations provided by expert physicians. Features by Scatnet and gold standard information are then used to train a Support Vector Machine. Results: The proposed CAD is tested on a new dataset of 1771 images annotated by three independent medical centers. The performances achieved by our CAD in recognizing positive, weak positive and negative samples are also compared against those obtained by other two approaches presented so far in the literature. The same system trained on this new dataset is then tested on two public datasets, namely MIVIA and I3Asel. Conclusions: The results confirm the effectiveness of our proposal, also revealing that it achieves the same performance as medical experts.

    关键词： HEp-2 samples,Deep learning,Invariant Scattering Convolutional Networks,Computer-aided diagnosis,Indirect immunofluorescence

    更新于2025-09-23 15:21:21

  • [IEEE 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - Chengdu, China (2018.3.26-2018.3.28)] 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - Noniterative Solution to Electromegnetic Inverse Scattering via Complex-Valued CNN

    摘要： This work presents a non-iterative approach to nonlinear electromagnetic inverse scattering by extending the conventional real-valued CNN technique to the complex-valued one. The approach can realize the very high quality image in a real-time way, leading to a very helpful tool of addressing the large-scale inverse scattering problem.

    关键词： Deep learning,Convolutional Neural network,Electromagnetic inverse scattering problem

    更新于2025-09-23 15:21:21

  • [IEEE 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - Chengdu (2018.3.26-2018.3.28)] 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - Complex-Valued Deep Convolutional Networks for Nonlinear Electromagnetic Inverse Scattering

    摘要： Electromagnetic inverse scattering problem is a typical complex problem while traditional deep convolutional neural network can only be applied to real problem. Motivated by this, this paper presents a new approach for electromagnetic inverse problem with complex convolutional neural network. In this way, several cascaded convolutional neural network modules are introduced to learn a model to realize super-resolution for electromagnetic imaging. The simulation and experimental results show that the proposed method paves a new way addressing real-time practical large-scale electromagnetic inverse scattering problems.

    关键词： super-resolution,electromagnetic imaging,convolutional neural network,electromagnetic inverse problem

    更新于2025-09-23 15:21:21

  • [IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Multi-View Bistatic Synthetic Aperture Radar Target Recognition Based on Multi-Input Deep Convolutional Neural Network

    摘要： Bistatic synthetic aperture radar (SAR) can provide additional observables and scattering information of the target from multiple views. In this paper, a new bistatic SAR automatic target recognition (ATR) method based on multi-input deep convolutional neural network is proposed. The geometry of the multi-view bistatic SAR ATR is modeled, and an electromagnetic simulation approach is utilized as an alternative to generate enough bistatic SAR images for network training. Then a deep convolutional neural network with multiple inputs is designed, and the features of the multi-view bistatic SAR images will be effectively learned by the proposed network. Therefore, the proposed method can achieve a superior recognition performance. Experimental results have shown the superiority of the proposed method based on the electromagnetic simulation bistatic SAR data.

    关键词： multi-view,deep convolutional neural network,automatic target recognition,Bistatic synthetic aperture radar

    更新于2025-09-23 15:21:21

  • Dual-Polarization Frequency Selective Rasorber With Independently Controlled Dual-Band Transmission Response

    摘要： It is of significant importance for any classification and recognition system, which claims near or better than human performance to be immune to small perturbations in the dataset. Researchers found out that neural networks are not very robust to small perturbations and can easily be fooled to persistently misclassify by adding a particular class of noise in the test data. This, so-called adversarial noise severely deteriorates the performance of neural networks, which otherwise perform really well on unperturbed dataset. It has been recently proposed that neural networks can be made robust against adversarial noise by training them using the data corrupted with adversarial noise itself. Following this approach, in this paper, we propose a new mechanism to generate a powerful adversarial noise model based on K-support norm to train neural networks. We tested our approach on two benchmark datasets, namely the MNIST and STL-10, using muti-layer perceptron and convolutional neural networks. Experimental results demonstrate that neural networks trained with the proposed technique show significant improvement in robustness as compared to state-of-the-art techniques.

    关键词： robustness,generalization,convolutional neural networks,adversarial,K-Support norm

    更新于2025-09-23 15:21:01

  • Deep Learning Enabled Strain Mapping of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-picometer Precision

    摘要： 2D materials offer an ideal platform to study the strain fields induced by individual atomic defects, yet challenges associated with radiation damage have so-far limited electron microscopy methods to probe these atomic-scale strain fields. Here, we demonstrate an approach to probe single-atom defects with sub-picometer precision in a monolayer 2D transition metal dichalcogenide, WSe2-2xTe2x. We utilize deep learning to mine large datasets of aberration-corrected scanning transmission electron microscopy images to locate and classify point defects. By combining hundreds of images of nominally identical defects, we generate high signal-to-noise class averages which allow us to measure 2D atomic spacings with up to 0.2 pm precision. Our methods reveal that Se vacancies introduce complex, oscillating strain fields in the WSe2-2xTe2x lattice that correspond to alternating rings of lattice expansion and contraction. These results indicate the potential impact of computer vision for the development of high-precision electron microscopy methods for beam-sensitive materials.

    关键词： scanning transmission electron microscopy,strain mapping,single-atom defects,Deep learning,fully convolutional network (FCN),2D materials

    更新于2025-09-23 15:21:01

  • Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Networka??Salp Swarm Algorithm

    摘要： The high utilization of renewable energy to manage climate change and provide green energy requires short-term photovoltaic (PV) power forecasting. In this paper, a novel forecasting strategy that combines a convolutional neural network (CNN) and a salp swarm algorithm (SSA) is proposed to forecast PV power output. First, the historical PV power data and associated weather information are classified into five weather types, such as rainy, heavy cloudy, cloudy, light cloudy and sunny. The CNN classification is then used to determine the prediction for the next day’s weather type. Five models of CNN regression are established to accommodate the prediction for different weather types. Each CNN regression is optimized using a salp swarm algorithm (SSA) to tune the best parameter. To evaluate the performance of the proposed method, comparisons were made to the SSA based support vector machine (SVM-SSA) and long short-term memory neural network (LSTM-SSA) methods. The proposed method was tested on a PV power generation system with a 500 kWp capacity located in south Taiwan. The results showed that the proposed CNN-SSA could accommodate the actual generation pattern better than the SVM-SSA and LSTM-SSA methods.

    关键词： convolutional neural network,salp swarm algorithm,renewable energy,day ahead forecasting,PV power forecasting

    更新于2025-09-23 15:21:01

  • [Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11259 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part IV) || Conductive Particles Detection in the TFT-LCD Manufacturing Process with U-ResNet

    摘要： The inspection of conductive particles after Anisotropic Conductive Film (ACF) bonding is a common and crucial step in the TFT-LCD manufacturing process since quality of conductive particles is an indicator of ACF bonding quality. Manual inspection under microscope is a time consuming and tedious work. There is a demand in industry for automatic conductive particle inspection system. The challenge of automatic conductive particle quality inspection is the complex background noise and diversified particle appearance, including shape, size, clustering and overlapping etc. As a result, there lacks effective automatic detection method to handle all the complex particle patterns. In this paper, we propose a U-shaped deep residual neural network (U-ResNet), which can learn features of particle from massive labeled data. The experimental results show that the proposed method achieves high accuracy and recall rate, which exceedingly outperforms the previous work.

    关键词： Deep convolutional network,Conductive particles,TFT-LCD,U-ResNet

    更新于2025-09-23 15:21:01