scholarly journals Correction: Liu et al. Research on Building DSM Fusion Method Based on Adaptive Spline and Target Characteristic Guidance. Information 2021, 12, 467

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Jinming Liu ◽  
Hao Chen ◽  
Shuting Yang
Keyword(s):  
Fusion Method ◽  
Target Characteristic ◽  
Guidance Information

Missing Citation [...]

scholarly journals Research on Building DSM Fusion Method Based on Adaptive Spline and Target Characteristic Guidance

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 467
Author(s):  
Jinming Liu ◽  
Hao Chen ◽  
Shuting Yang
Keyword(s):  
Geometric Structure ◽  
3D Model ◽  
Structural Characteristics ◽  
Model Structure ◽  
Fusion Method ◽  
Structure Information ◽  
Practical Engineering ◽  
Target Characteristic ◽  
Target Characteristics ◽  
Constraint Method

In order to adapt to the actual scene of a stereo satellite observing the same area sequentially and improve the accuracy of the target-oriented 3D reconstruction, this paper proposed a building DSM fusion update method based on adaptive splines and target characteristic guidance. This method analyzed the target characteristics of surface building targets to explore their intrinsic geometric structure information, established a nonlinear fusion method guided by the target characteristics to achieve the effective fusion of multiple DSMs on the basis of maintaining the target structural characteristics, and supported the online updating of DSM to ensure the needs of practical engineering applications. This paper presented a DSM fusion method for surface building targets and finally conducted DSM fusion experiments using typical urban area images of different scenes. The experimental results showed that the proposed method can effectively constrain and improve the DSM of buildings, and the integrity of the overall construction of the target 3D model structure was significantly improved, indicating that this paper provides an effective and efficient DSM constraint method for buildings.

Active Breadcrumbs: Adaptive Distribution of In-Network Guidance Information for Content-Oriented Networks

2013 ◽  
Vol E96.B (7) ◽  
pp. 1670-1679 ◽  
Author(s):  
Masayuki KAKIDA ◽  
Yosuke TANIGAWA ◽  
Hideki TODE
Keyword(s):  
Guidance Information

Color fusion method for night vision based on YUV space

2011 ◽  
Vol 30 (12) ◽  
pp. 3222-3224
Author(s):  
Xiao-yan QIAN ◽  
Lei HAN ◽  
Bang-feng WANG
Keyword(s):  
Night Vision ◽  
Fusion Method ◽  
Color Fusion

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

Image Fusion Method Based on Multi-scale Directional Fast Guided Filter and Convolutional Sparse Representation

2019 ◽  
Vol 12 (3) ◽  
pp. 263-269
Author(s):  
Liu Xian-Hong ◽  
Chen Zhi-Bin
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Filter Bank ◽  
Guided Filter ◽  
Fusion Method ◽  
Edge Preserving ◽  
Directional Information ◽  
Multi Scale ◽  
Directional Filter ◽  
Image Fusion Method

Background: A multi-scale multidirectional image fusion method is proposed, which introduces the Nonsubsampled Directional Filter Bank (NSDFB) into the multi-scale edge-preserving decomposition based on the fast guided filter. Methods: The proposed method has the advantages of preserving edges and extracting directional information simultaneously. In order to get better-fused sub-bands coefficients, a Convolutional Sparse Representation (CSR) based approximation sub-bands fusion rule is introduced and a Pulse Coupled Neural Network (PCNN) based detail sub-bands fusion strategy with New Sum of Modified Laplacian (NSML) to be the external input is also presented simultaneously. Results: Experimental results have demonstrated the superiority of the proposed method over conventional methods in terms of visual effects and objective evaluations. Conclusion: In this paper, combining fast guided filter and nonsubsampled directional filter bank, a multi-scale directional edge-preserving filter image fusion method is proposed. The proposed method has the features of edge-preserving and extracting directional information.

Reinforcement learning based data fusion method for multi-sensors

2020 ◽  
Vol 7 (6) ◽  
pp. 1489-1497
Author(s):  
Tongle Zhou ◽  
Mou Chen ◽  
Jie Zou
Keyword(s):  
Reinforcement Learning ◽  
Data Fusion ◽  
Fusion Method

SARF: A Simple, Adjustable, and Robust Fusion Method

2021 ◽  
pp. 1-5
Author(s):  
Xiangchao Meng ◽  
Gang Yang ◽  
Feng Shao ◽  
Weiwei Sun ◽  
Huanfeng Shen ◽  
...  
Keyword(s):  
Fusion Method

scholarly journals Relative Total Variation Structure Analysis-Based Fusion Method for Hyperspectral and LiDAR Data Classification

2021 ◽  
Vol 13 (6) ◽  
pp. 1143
Author(s):  
Yinghui Quan ◽  
Yingping Tong ◽  
Wei Feng ◽  
Gabriel Dauphin ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Total Variation ◽  
Structure Analysis ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Heterogeneous Data ◽  
Lidar Data ◽  
Fusion Method ◽  
Wide Range ◽  
Relative Total Variation

The fusion of the hyperspectral image (HSI) and the light detecting and ranging (LiDAR) data has a wide range of applications. This paper proposes a novel feature fusion method for urban area classification, namely the relative total variation structure analysis (RTVSA), to combine various features derived from HSI and LiDAR data. In the feature extraction stage, a variety of high-performance methods including the extended multi-attribute profile, Gabor filter, and local binary pattern are used to extract the features of the input data. The relative total variation is then applied to remove useless texture information of the processed data. Finally, nonparametric weighted feature extraction is adopted to reduce the dimensions. Random forest and convolutional neural networks are utilized to evaluate the fusion images. Experiments conducted on two urban Houston University datasets (including Houston 2012 and the training portion of Houston 2017) demonstrate that the proposed method can extract the structural correlation from heterogeneous data, withstand a noise well, and improve the land cover classification accuracy.

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