- 标题
- 摘要
- 关键词
- 实验方案
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[IEEE 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - Chengdu (2018.3.26-2018.3.28)] 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - High Resolution 2d-Imaging Based on Data Fusion Technique
摘要： The range resolution of the traditional single radar imaging system is limited by the bandwidth of the transmitted signal, while the cross resolution is limited by its observation angle range. In this paper, a high resolution 2d-imaging method using data fusion technique is proposed. First, we introduce the theoretical basis of multi-radar data fusion imaging based on the 2d-radar echo sparse representation model. Then, sparse parameters of multi-radar echo are obtained by ExCoV algorithm. Finally, we get lost echo data by interpolation and extrapolation and realize the fusion process. The simulation results show that the image quality is improved after radar data fusion, which is better than that of the single radar echo, verifying the effectiveness of our method.
关键词： ExCoV,high resolution,2d-imaging,data fusion
更新于2025-09-23 15:23:52
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[IEEE 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) - Singapore, Singapore (2018.11.18-2018.11.21)] 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) - Downside Hemisphere Object Detection and Localization of MAV by Fisheye Camera
摘要： For a multirotor micro aerial vehicle (MAV) flying in the outdoor environment, its downside hemisphere has richest visual information. All that information can be obtained by a single fisheye camera with larger than 180 degrees field of view (FOV). Traditionally, the unrestored fisheye image is restored to a flat image before subsequent processing, which is both resource and time consuming. In this paper, to save resource and time, a method of fisheye object detection and localization on the unrestored fisheye image is proposed. A single-stage neural network is built for object detection. To improve the performance of detector, its submodules are designed specifically by combining the central rotational property and severe distortion of the fisheye image. To meet the real-time requirements of onboard computation, the detector is also tuned to be light-weight. After that, the detected objects are localized with assistance of by a data fusion on the fisheye model and MAV sensory data (altitude, attitude, etc.). The experimental results have validated the effectiveness of the proposed methods in this paper.
关键词： deep learning,object detection,data fusion
更新于2025-09-23 15:23:52
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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 - Integration of Worldview-2 and Lidar Data to MAP a Subtropical Forest Area: Comparison of Machine Learning Algorithms
摘要： This work is committed to explore the integration of airborne LiDAR data and WorldView-2 (WV-2) images to classify land cover and land use in a rural area with the presence of a subtropical forest. Different methods were used for this purpose: two artificial neural networks (ANN) and three decision trees forests. The results demonstrated that the inclusion of LiDAR data significantly improved the classifications in all methods. Excluding the Convolutional Neural Network, the classification algorithms had a nearly similar performance, and none of them achieved the best accuracy for all adopted classes. Forest by Penalizing Attributes (FPA) attained the best general result, with a Kappa index of 0.92, while Rotation Forest obtained the best result in the classification of the two vegetation classes.
关键词： Artificial Neural Network,Data fusion,Forest succession stages,Decision Forest
更新于2025-09-23 15:22:29
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Data fusion strategy in quantitative analysis of spectroscopy relevant to olive oil adulteration
摘要： Olive oil adulteration with various less expensive edible oils represents a great danger for consumers. Spectrometry has been used to detect olive oil adulteration with other oil, but we need more robust and accurate model. Therefore, this work investigated the combination of infrared (NIR) and mid infrared (MIR) spectroscopy for the quantification of rapeseed oil in olive oil blends. Furthermore, a partial least squares (PLS) model was established to predict the concentration of the adulterant. Models constructed using baseline correction by combination of standard normal variate (SNV), SG smoothing and vector normalization pretreatments, respectively. Three data fusion strategies (low, mid and high-level) have been applied to take advantage of the synergistic effect of the information obtained from NIR and MIR. We chose algorithm (SPA) to extract spectral features for mid-level data fusion. Binary linear regression used in high-level data fusion. We selected the best pretreatment for final evaluation according to the evaluation parameters (R2 of calibration and validation, RMSECV and RMSEP). NIR, MIR and data fusion models were evaluated by comparing the R2 of validation and RMSEP (root mean square error of prediction). The RMSEP of low-level (3.44) , high-level (2.86) data fusion were better than NIR（7.09）, MIR（4.04）, mid-level（6.09）and the validation coefficient of determination R2 of low-level data fusion (0.975) and high-level data fusion (0.988) are better than the NIR (0.896) and MIR (0.966). Results showed that：(1) NIR and MIR are fast and non-destructive testing tools to detect the extra-virgin olive oil adulteration with rapeseed oil. (2) Low-level data fusion can effectively improve model prediction accuracy. (3) SPA reduced the number of variables, but it did not improved the results. (4) High-level data fusion strategy can be used as a reliable tool for quantitative analysis.
关键词： Olive oil,data fusion,SPA,MIR,Adulteration,NIR
更新于2025-09-23 15:22:29
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[IEEE 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) - Nantes, France (2018.9.24-2018.9.27)] 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) - Bluetooth-Based Indoor Positioning Through ToF and RSSI Data Fusion
摘要： After several decades of both market and scientific interest, indoor positioning is still a hot and not completely solved topic, fostered by the advancement of technology, pervasive market penetration of mobile devices and novel communication standards. In this work, we propose a two-step model-based indoor positioning algorithm based on Bluetooth Low-Energy, a pervasive and energy efficient standard protocol. In the first (i.e. ranging) step a Kalman Filter (KF) performs the fusion of both RSSI and Time-of-Flight measurement data. Thus, we demonstrate the benefit of not relying only on RSSI, comparing ranging performed with or without the help of ToF. In the second (i.e. positioning) step, the distance estimates from multiple anchors are combined into a quadratic cost function, which is minimized to determine the coordinates of the target node in a planar reference frame. The proposed solution is tailored to reduce the computational effort and target real-time execution on an embedded platform, demonstrating a limited loss of performance. The paper presents an experimental setup and discusses meaningful results, demonstrating a robust BLE-based indoor positioning solution for embedded systems.
关键词： Time-of-Flight (ToF),Bluetooth Low Energy,Indoor positioning,data fusion,Kalman filter
更新于2025-09-23 15:21:21
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[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 - Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds
摘要： The increasingly availability of Light Detection and Ranging (LiDAR) data acquired at different times can be used to analyze the forest dynamics at individual tree level. This often requires to deal with LiDAR point clouds having significantly different point densities. To address this issue, this paper presents a method for the fusion of multitemporal LiDAR data which aims at using the information provided by high density LiDAR data (higher than 10 pts/m2) to improve the single tree parameter estimation of low density data (up to 5 pts/m2) acquired over the same forest at different times. The method first accurately characterizes the crown shapes on the high density data. Then, it uses the obtained estimates to drive the tree parameter estimation on the low density LiDAR data. The method has been tested on a multitemporal dataset acquired in coniferous forests located in the Italian Alps. Experimental results confirmed the effectiveness of the method.
关键词： Point Cloud,Tree Crown Parameters,Remote Sensing,Multitemporal LiDAR Data,Data Fusion
更新于2025-09-23 15:21:01
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Evaluation of calibration methods to construct a 3-D environmental map with good color projection using both camera images and laser scanning data
摘要： In mobile robot navigation, restoration of the environment around the robot in a 3-D map is necessary for self-location, route planning, and detecting surrounding obstacles. We construct the 3-D color environmental map using both camera images and laser scanner (LIDAR) point cloud data. In the map, the RGB values are provided to the LIDAR point cloud data by projecting the point cloud onto the simultaneously acquired image. The projection parameters can be determined by measuring the calibration boards using both the camera and the LIDAR. In this paper, we have constructed a method to evaluate the accuracy of projection applicable to any calibration methods. And, then, we found that the calibration points in the central position of an image are important to obtain good projection parameters and that additional points at side positions also can improve the accuracy of the projection.
关键词： 3-D mapping,SLAM,Calibration,Data fusion
更新于2025-09-23 15:21:01
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[IEEE 2018 International Conference on System Science and Engineering (ICSSE) - New Taipei City, Taiwan (2018.6.28-2018.6.30)] 2018 International Conference on System Science and Engineering (ICSSE) - Optical SAR Fusion of Sentinel-2 Images for Mapping High Resolution Land Cover
摘要： Sentinel-2 is a very new programme of the European Space Agency (ESA) that is designed for fine spatial resolution global monitoring. Land cover–land use (LCLU) classification tasks can take advantage of the fusion of radar and optical remote sensing data, leading generally to increase mapping accuracy. Here we propose a methodological approach to fuse information from the new European Space Agency Sentinel-1 and Sentinel-2 imagery for accurate land cover mapping of a portion of the South Solok region, West Sumatera. Data pre-processing was carried out using the European Space Agency’s Sentinel Application Platform and the SEN2COR toolboxes. The two main objectives of this study are to evaluate the potential use and synergetic effects of ESA Sentinel-1A C-band SAR and Sentinel-2A Optical data for classification and mapping of LCLU. As a result of the research, two main advantages. First, the pre-processing chain supported by sensor-specific toolboxes developed by ESA represents a reliable and fast approach for the preparation of ready-to-process imagery. Second, investigation to derive a methodological framework to integrate Sentinel-1 and Sentinel-2 imagery for land cover mapping by integrating of radar and optical imagery have been set up and tested.
关键词： segmentation,Sentinel-1,SAR,South Solok,Sentinel-2,land cover mapping,data fusion
更新于2025-09-23 15:21:01
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Spectral Image Fusion from Compressive Measurements
摘要： Compressive spectral imagers reduce the number of sampled pixels by coding and combining the spectral information. However, sampling compressed information with simultaneous high spatial and high spectral resolution demands expensive high-resolution sensors. This work introduces a model allowing data from high spatial/low spectral and low spatial/high spectral resolution compressive sensors to be fused. Based on this model, the compressive fusion process is formulated as an inverse problem that minimizes an objective function defined as the sum of a quadratic data fidelity term and smoothness and sparsity regularization penalties. The parameters of the different sensors are optimized and the choice of an appropriate regularization is studied in order to improve the quality of the high resolution reconstructed images. Simulation results conducted on synthetic and real data, with different CS imagers, allow the quality of the proposed fusion method to be appreciated.
关键词： Spectral imaging,data fusion,remote sensing,compressive sampling
更新于2025-09-23 15:21:01
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Simple Cosolvent-Treated PEDOT:PSS Films on Hybrid Solar Cells With Improved Efficiency
摘要： This paper reports the outcomes of the 2014 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource remote sensing studies. In the 2014 edition, participants considered multiresolution and multi-sensor fusion between optical data acquired at 20-cm resolution and long-wave (thermal) infrared hyperspectral data at 1-m resolution. The Contest was proposed as a double-track competition: one aiming at accurate landcover classification and the other seeking innovation in the fusion of thermal hyperspectral and color data. In this paper, the results obtained by the winners of both tracks are presented and discussed.
关键词： multimodal-,multisource-data fusion,thermal imaging,landcover classification,multiresolution-,Hyperspectral,image analysis and data fusion (IADF)
更新于2025-09-23 15:19:57