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- 关键词
- 实验方案
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过滤筛选
- 2018
- Conditional Random Fields (CRF)
- Convolutional Neural Network (CNN)
- Fine Classification
- Airborne hyperspectral
- green tide
- Elegant End-to-End Fully Convolutional Network (E3FCN)
- deep learning
- remote sensing
- Moderate Resolution Imaging Spectroradiometer (MODIS)
- Optoelectronic Information Science and Engineering
- Ocean University of China
- Wuhan University
- Central South University
- Hubei University
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Semantic segmentation of high spatial resolution images with deep neural networks
摘要： Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach.
关键词： pyramid pooling,deep learning,global context information,high-resolution image segmentation,residual network
更新于2025-09-23 15:22:29
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[IEEE 2018 Asia Communications and Photonics Conference (ACP) - Hangzhou, China (2018.10.26-2018.10.29)] 2018 Asia Communications and Photonics Conference (ACP) - A Marketplace for Real-time Virtual PON Sharing
摘要： We propose a marketplace where multiple network operators coexisting on the same passive optical network (PON) infrastructure can share their excess capacity. We designed a double auction to assure efficient allocation of optical access capacity.
关键词： Network sharing,Passive Optical Networks,Auction,Infrastructure sharing
更新于2025-09-23 15:22:29
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Fusion of Template Matching and Foreground Detection for Robust Visual Tracking
摘要： In this paper, we present an end-to-end framework for visual tracking that contains fully convolutional template matching network and fully convolutional foreground detection network. It fuses the response maps of foreground detection and template matching for robust tracking and it can inherits all the merits of them. Besides, our network don't need additional datasets to train and only object information in the first frame is needed in training stage. We conduct extensive experiments on OTB2013 and OTB2015 and our tracker achieves state-of-the-art performance in both efficiency and accuracy.
关键词： End-to-end,Matching network,Foreground detection,Visual tracking
更新于2025-09-23 15:22:29
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[IEEE 2018 20th International Conference on Transparent Optical Networks (ICTON) - Bucharest (2018.7.1-2018.7.5)] 2018 20th International Conference on Transparent Optical Networks (ICTON) - Enhanced Capacity of Radio over Fiber Links Using Polarization Multiplexed Signal Transmission
摘要： The future vision of integrating back- and front-haul in 5G mobile networks requires the design of high capacity transport architectures. To achieve this progress we suggest the application of the radio over fiber (RoF) technology for 5G back- and front-haul access links. Our proposed design is based on the coherent polarization multiplexed (Pol-Mux) technique. To understand the properties of polarization mode dispersion (PMD) we present the impact of PMD on the link quality factor with varying fiber length, bit rate, and PMD coefficient. To increase the capacity of the radio over fiber link we propose a new conversion method utilizing polarization controllers and the polarization beam combiners. Applying the new method 40 Gbit/s QPSK and 16-QAM signals are converted to coherent polarization multiplexed (Pol-Mux) signals. In the Pol-Mux approach the polarization cross talk can cause a significant impairment. However, with sufficient polarization extinction ratio this effect can be reduced substantially. Our simulation results reveal that the Pol-Mux approach yields better performance e.g. in terms of high data rates, and this way can significantly increase the link capacity for 5G access and core network functions.
关键词： QPSK,PMD,Pol-Mux,16-QAM,5G mobile network
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Multi-Direction Subbands and Deep Neural Networks Bassed Pan-Sharpening Method
摘要： This paper proposes a pan-sharpening method based on multi-direction subbands and deep neural networks. First, by utilizing the multi-scale and multi-direction properties of the nonsubsampled contourlet transform (NSCT), panchromatic (PAN) image is decomposed into the low frequency subbands in different resolutions and the high frequency subbands in different directions. Pan-sharpening method aims to fuse the high frequency subband coefficients of PAN image and the low frequency subband coefficients of multispectral (MS) image. Second, in order to better extract the feature of the high frequency subbands in different directions of PAN image, the deep neural network (DNN) is trained using the image patches of high frequency subbands of PAN image. Third, in the fusion stage, we exploit NSCT on the principal component of resampled low resolution (LR) MS image. The high frequency subbands of output high resolution (HR) MS image is obtained by forward propagation of the trained DNN, which input is the high frequency subbands of LR MS image. Finally, a new subband set is obtained by fusing the reconstructed high frequency subband and the original low frequency subband of LR MS image. The HR MS image is produced by executing the inverse transform of NSCT and adaptive PCA (A-PCA) on the new subband set. The experimental results show the proposed method outperforms other well-known methods in terms of both objective measurements and visual evaluation.
关键词： adaptive Principal Component Analysis (A-PCA),deep neural network (DNN),pan-sharpening,nonsubsampled contourlet transform (NSCT)
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Proposal of Millimeter-Wave Adaptive Glucose-Concentration Estimation System Using Complex-Valued Neural Networks
摘要： This paper presents a novel approach for glucose concentration detection using a complex-valued neural network (CVNN) based on microwave transmission characteristics. The method leverages the dielectric properties of glucose solutions, which vary with concentration, to train a neural network that accurately predicts glucose levels from S-parameter measurements. Experimental results demonstrate high accuracy and robustness across a range of concentrations from 0 to 300 mg/dL.
关键词： complex-valued neural network,dielectric properties,glucose detection,S-parameters,microwave sensing
更新于2025-09-23 15:22:29
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Hyperspectral Coastal Wetland Classification Based on a Multiobject Convolutional Neural Network Model and Decision Fusion
摘要： The phenomenon of spectral aliasing exists for coastal wetland object types, which leads to class mixing. This letter proposes a multiobject convolutional neural network (CNN) decision fusion classification method for hyperspectral images of coastal wetlands. This method adopts decision fusion based on fuzzy membership rules applied to single-object CNN classification to obtain higher classification accuracy. Experimental results demonstrate the effectiveness of the proposed method for the six object types, including water, tidal flat, reed, and other vegetation types. The overall accuracy of the decision fusion classification method based on fuzzy membership is 82.11%, which is 3.33% and 6.24% higher than those of single-object feature band CNN and support vector machine methods. The classification method based on multiobject CNN decision fusion inherits the characteristics of single-object feature bands of the CNN, making it a practical approach to image classification under the challenging conditions in which class mixing occurs.
关键词： decision fusion,convolutional neural network (CNN),hyperspectral image,Classification
更新于2025-09-23 15:22:29
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Design of Real-Time Slope Monitoring system using Time-Domain Reflectometry with Wireless Sensor Network
摘要： Slope monitoring systems using WSN is an efficient technique to track slope movements or failures. Instability in slope contributes greatly in hazards caused to lives and property in mining areas and operations. Electronic instrumentation like wire-line extensometers, piezometers, total stations etc. are used for sensing the slope stability. The available wireless monitoring systems like SSR, LiDAR, Laser monitoring techniques are more expensive. In order to overcome this, efficient and economically feasible measurement systems for slope monitoring are needed. This paper presents the design, development and field implementation of an online, cost-effective wireless system for real-time slope monitoring which gives visible warning of a possible slope failure. The system is designed on Arduino board as a platform with RF module (ZigBee) for wireless communication. Additionally, using python the database and GUI are implemented. The experimental field TDR data is also verified with the total station monitoring system.
关键词： ZigBee,slope monitoring,time domain reflectometry (TDR),Coaxial cable,wireless sensor network (WSN)
更新于2025-09-23 15:22:29
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A Novel Neural Network for Remote Sensing Image Matching
摘要： Rapid development of remote sensing (RS) imaging technology makes the acquired images have larger size, higher resolution, and more complex structure, which goes beyond the reach of classical hand-crafted feature-based matching. In this paper, we propose a feature learning approach based on two-branch networks to transform the image matching task into a two-class classification problem. To match two key points, two image patches centered at the key points are entered into the proposed network. The network aims to learn discriminative feature representations for patch matching, so that more matching pairs can be obtained on the premise of maintaining higher subpixel matching accuracy. The proposed network adopts a two-stage training mode to deal with the complex characteristics of RS images. An adaptive sample selection strategy is proposed to determine the size of each patch by the scale of its central key point. Thus, each patch can preserve the texture structure around its key point rather than all patches have a predetermined size. In the matching prediction stage, two strategies, namely, superpixel-based sample graded strategy and superpixel-based ordered spatial matching, are designed to improve the matching efficiency and matching accuracy, respectively. The experimental results and theoretical analysis demonstrate the feasibility, robustness, and effectiveness of the proposed method.
关键词： neural network,image matching,remote sensing (RS) image,Deep learning (DL)
更新于2025-09-23 15:22:29
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A Pipeline Neural Network For Low-Light Image Enhancement
摘要： Low-light image enhancement is an important challenge in computer vision. Most of low-light images taken in low-light conditions usually look noisy and dark, which makes it more difficult for subsequent computer vision tasks. In this paper, inspired by multi-scale retinex, we present a low-light image enhancement pipeline network based on an end-to-end fully convolutional networks and discrete wavelet transformation (DWT). Firstly, we show that Multi Scale Retinex (MSR) can be considered as a convolutional neural network (CNN) with Gaussian convolution kernel and blending the result of DWT can improve the image produced by MSR. Secondly, we propose our pipeline neural network, consisting of denoising net and low light image enhancement net (LLIE-net) which learns a function from a pair of dark and bright images. Finally, we evaluate our method both in synthetic dataset and public dataset. Experiments reveal that in comparison with other state-of-the-art methods, our methods achieve better performance in the perspective of qualitative and quantitative analysis.
关键词： Convolutional Neural Network,LLIE-Net,Low-light image enhancement
更新于2025-09-23 15:22:29