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oe1(光电查) - 科学论文

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出版时间
  • 2018
研究主题
  • Conditional Random Fields (CRF)
  • Convolutional Neural Network (CNN)
  • Fine Classification
  • Airborne hyperspectral
应用领域
  • Optoelectronic Information Science and Engineering
机构单位
  • Wuhan University
  • Central South University
  • Hubei University
404 条数据
?? 中文（中国） ▾

  • [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 - Introducing Eurosat: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

    摘要： In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The key contributions are as follows. We present a novel dataset based on Sentinel-2 satellite images covering 13 different spectral bands and consisting of 10 classes with in total 27,000 labeled images. We evaluate state-of-the-art deep Convolutional Neural Networks (CNNs) on this novel dataset with its different spectral bands. We also evaluate deep CNNs on existing remote sensing datasets and compare the obtained results. With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The classification system resulting from the proposed research opens a gate towards various Earth observation applications. We demonstrate how the classification system can assist in improving geographical maps.

    关键词： Deep Learning,Land Use Classification,Earth Observation,Convolutional Neural Network,Machine Learning,Dataset,Land Cover Classification

    更新于2025-09-23 15:21:21

  • [IEEE 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Singapore (2018.5.22-2018.5.25)] 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Solar Plant Integration to Utility Grid with Improved Power Quality by using RNN-Hebbian-LMS Current Controller

    摘要： In this paper a new topology of current control technique is proposed, to transfer the improved quality of power produced from the solar plant to the utility grid. Also, a high-gain high-efficient converter driving with Kalman MPPT is used to boost the low voltage levels of the PV array. The proposed control uses recurrent neural network RNN-Hebbian -LMS based current controller to achieve the better performance in terms of power quality. The RNN network uses feedback signals to control the current flow from solar plant to the utility grid. The Hebbian - LMS (least mean square) algorithm is used to update the weights of the RNN based current controller. The main advantage of the RNN-Hebbian-LMS current control technique is to maintain the constant voltage. Besides, it also provides system stability over wide range of parameter variations and damp out the oscillations quickly. The proposed algorithm is able to overcome the stability and sensitivity problems incurred with the conventional PI current controller. The simulation results will be compared with the conventional PI and proposed RNN-Hebbiab-LMS current controllers. Finally, the proposed current controller shows the improved power quality, quick settling time and more stable comparing with the conventional controllers.

    关键词： Least Mean Square (LMS),Utility Grid,Solar Plant,High-Gain converter,PV array,Maximum Power Point Tracking (MPPT),Recurrent Neural Network (RNN)

    更新于2025-09-23 15:21:21

  • Deep convolutional representations and kernel extreme learning machines for image classification

    摘要： Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image classification and related tasks. However, the fully-connected layers in CNN are not robust enough to serve as a classifier to discriminate deep convolutional features, due to the local minima problem of back-propagation. Kernel Extreme Learning Machines (KELMs), known as an outstanding classifier, can not only converge extremely fast but also ensure an outstanding generalization performance. In this paper, we propose a novel image classification framework, in which CNN and KELM are well integrated. In our work, Densely connected network (DenseNet) is employed as the feature extractor, while a radial basis function kernel ELM instead of linear fully connected layer is adopted as a classifier to discriminate categories of extracted features to promote the image classification performance. Experiments conducted on four publicly available datasets demonstrate the promising performance of the proposed framework against the state-of-the-art methods.

    关键词： Extreme learning machine,Neural network,Image classification

    更新于2025-09-23 15:21:21

  • 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 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

  • Cloud Masking Technique for High-Resolution Satellite Data: An Artificial Neural Network Classifier Using Spectral & Textural Context

    摘要： Cloud masking is a very important application in remote sensing and an essential pre-processing step for any information derivation applications. It helps in estimation of usable portion of the images. Many popular spectral classi?cation techniques rely upon the presence of a short-wave infrared band or bands of even higher wavelength to differentiate between clouds and other land covers. However, these methods are limited to sensors equipped with higher wavelength bands. In this paper, a generic and ef?cient technique is attempted using the Cartosat-2 series (C2S) satellite which is having high-resolution multispectral sensor in the visible and near-infrared bands. The methodology is based on textural features from the available spectral context, and using a feedforward neural network for the classi?cation is proposed. The method was shown to have an overall accuracy of 97.98% for a large manually pre-classi?ed validation dataset with more than 2 million data points. Experimental results and cloud masks generated for various scenes show that the method may be viable as a reasonable cloud masking algorithm for C2S data.

    关键词： Cloud masking,Feed forward network,High-resolution satellite data,Image classi?cation,Arti?cial neural network,GLCM texture

    更新于2025-09-23 15:21:01

  • [IEEE 2018 IEEE 3rd International Conference on Cloud Computing and Internet of Things (CCIOT) - Dalian, China (2018.10.20-2018.10.21)] 2018 IEEE 3rd International Conference on Cloud Computing and Internet of Things (CCIOT) - Maximum Power Point Tracking Algorithm for Laser Power Beaming Based on Neural Networks

    摘要： With the high voltage intelligent substation developing at a pretty high speed, more and more artificial intelligent techniques have been incorporated into the power devices to meet the automation needs. For the sake of the line maintenance staff's safety, the high voltage isolating switch draws great attention among the most important power devices because of its capability of connecting and disconnecting the high voltage circuit. However, due to high-voltage isolating switch's working environment, the power supply system of the surveillance devices could suffer from great electromagnetic interference. Laser power beaming exhibits its merits in such situation because it can provide steady power from a distance despite the day or the night. Then the energy conversion efficiency arises as a new concern. To make full use of the laser power, this paper mainly focuses on extracting the maximum power from the photovoltaic (PV) panel. And a neural network based algorithm is proposed which combines both the intrinsic and the extrinsic features of the PV panel with the proportion of the voltage of the maximum power point (MPP) to the open circuit voltage of the PV panel. Simulations and experiments are carried out to verify the validness and feasibility of the algorithm.

    关键词： laser power beaming,maximum power point,neural network,photovoltaic panel

    更新于2025-09-23 15:21:01

  • Maximum Power Point tracking for a stand-alone photovoltaic system using Artificial Neural Network

    摘要： This paper presents an intelligent method to extract the maximum power from the photovoltaic panel using artificial neural network (ANN). The inputs data required for training the ANN controller are obtained from real weather conditions and the desired output is obtained from perturb and observe (P&O) method. The proposed model is capable to improve the dynamic response and steady-state performance of the system, provides an accurate identification of the optimal operating point and an accurate estimation of the maximum power from the photovoltaic panels. The proposed ANN model is compared with conventional P&O model and shown that ANN controller could increase the power output by approximately 20%. The system is simulated and studied using MATLAB software.

    关键词： Artificial Neural Network,Maximum Power Point tracking,photovoltaic system,P&O method,MATLAB

    更新于2025-09-23 15:21:01