scholarly journals Geographical Scene Reconstruction and Application of Ancient Site with Fused Multi-source Data

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Lin Yang ◽  
Jiangwei Shen ◽  
Fangzihao Zheng
Keyword(s):  
Data Fusion ◽  
Archaeological Site ◽  
Temporal Analysis ◽  
Fusion Method ◽  
Time Data ◽  
Seamless Integration ◽  
Human Environment ◽  
Source Data ◽  
Spatio Temporal ◽  
Functional Areas

<p><strong>Abstract.</strong> As important places and cultural relics retained by the ancients in production and activities, ancient sites have internal geographical feature and differentiation laws under the interaction of long-term human-environment relationship. Thereof, aiming to the current relative research shortage of geographical background and space-time frame, the research goal of multi-level geographical parsing for archaeological site was proposed in this paper. By summarizing the characteristics of multi-source spatio-temporal data, we studied on the data fusion method and building the whole archaeological site considering total scene factors and knowledge rules, as well as the related application research. The Lingjiatan Site was adopted as the study area in this paper, and we reconstructed its 3D geographical scene with the surveying and excavation data in the past 30 years. Furthermore, we attempted to study the spatial morphology, patterns and arrangement on different levels and scales about the site.</p><p>Methodology: the data fusion method proposed in this paper was as Figure1. The multi-source data included UAV images, LiDAR data, archaeological drilling and excavation data, basic geographical data and so on. On basis of the completed spatio-temporal elements information, aiming at the characteristic of these data, we put forward the methods of data pre-processing and data fusion, which involved registration, space interpolation, raster calculation, Boolean operation, 3D modelling and so on. The key research focused on the seamless integration and uniform data maintenance of these multi dimensions and multi-time data. Ultimately, we constructed the holographic geographical scene of the historical site, which provided a spatio-time platform for the subsequent geographical analysis and primary application.</p><p>Analysis and primary application: the geographical scene of Lingjiatan Site was as figure 2. Combining with the archaeological data, we could carry out some primary spatio-temporal analysis: (1) the distribution of the functional areas (three kinds of relics: sacrificial area, living areas and trenches) and the relationship with the natural environment; (2) the spatio-temporal relationship among the functional areas; (3) by simulating the optimal paths of several typical relics (the details were as following figure 3 showed ), reasoning for the ancients’ behaviour patterns, and providing the forecasting and verification for field archaeology.</p>

scholarly journals Multi-source data fusion method for structural safety assessment of water diversion structures

2021 ◽  
Author(s):  
Sherong Zhang ◽  
Ting Liu ◽  
Chao Wang
Keyword(s):  
Data Fusion ◽  
Safety Assessment ◽  
Evidence Theory ◽  
Weighted Average ◽  
Water Diversion ◽  
Sensor Data ◽  
Structural Safety ◽  
Fusion Method ◽  
Bhattacharyya Distance ◽  
Source Data

Abstract Building safety assessment based on single sensor data has the problems of low reliability and high uncertainty. Therefore, this paper proposes a novel multi-source sensor data fusion method based on Improved Dempster–Shafer (D-S) evidence theory and Back Propagation Neural Network (BPNN). Before data fusion, the improved self-support function is adopted to preprocess the original data. The process of data fusion is divided into three steps: Firstly, the feature of the same kind of sensor data is extracted by the adaptive weighted average method as the input source of BPNN. Then, BPNN is trained and its output is used as the basic probability assignment (BPA) of D-S evidence theory. Finally, Bhattacharyya Distance (BD) is introduced to improve D-S evidence theory from two aspects of evidence distance and conflict factors, and multi-source data fusion is realized by D-S synthesis rules. In practical application, a three-level information fusion framework of the data level, the feature level, and the decision level is proposed, and the safety status of buildings is evaluated by using multi-source sensor data. The results show that compared with the fusion result of the traditional D-S evidence theory, the algorithm improves the accuracy of the overall safety state assessment of the building and reduces the MSE from 0.18 to 0.01%.

scholarly journals A Simple Spatio–Temporal Data Fusion Method Based on Linear Regression Coefficient Compensation

2020 ◽  
Vol 12 (23) ◽  
pp. 3900
Author(s):  
Bingxin Bai ◽  
Yumin Tan ◽  
Gennadii Donchyts ◽  
Arjen Haag ◽  
Albrecht Weerts
Keyword(s):  
Linear Regression ◽  
Data Fusion ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Fusion Method ◽  
Temporal Data ◽  
Resolution Image ◽  
Coarse Resolution ◽  
Fine Spatial Resolution ◽  
Spatio Temporal

High spatio–temporal resolution remote sensing images are of great significance in the dynamic monitoring of the Earth’s surface. However, due to cloud contamination and the hardware limitations of sensors, it is difficult to obtain image sequences with both high spatial and temporal resolution. Combining coarse resolution images, such as the moderate resolution imaging spectroradiometer (MODIS), with fine spatial resolution images, such as Landsat or Sentinel-2, has become a popular means to solve this problem. In this paper, we propose a simple and efficient enhanced linear regression spatio–temporal fusion method (ELRFM), which uses fine spatial resolution images acquired at two reference dates to establish a linear regression model for each pixel and each band between the image reflectance and the acquisition date. The obtained regression coefficients are used to help allocate the residual error between the real coarse resolution image and the simulated coarse resolution image upscaled by the high spatial resolution result of the linear prediction. The developed method consists of four steps: (1) linear regression (LR), (2) residual calculation, (3) distribution of the residual and (4) singular value correction. The proposed method was tested in different areas and using different sensors. The results show that, compared to the spatial and temporal adaptive reflectance fusion model (STARFM) and the flexible spatio–temporal data fusion (FSDAF) method, the ELRFM performs better in capturing small feature changes at the fine image scale and has high prediction accuracy. For example, in the red band, the proposed method has the lowest root mean square error (RMSE) (ELRFM: 0.0123 vs. STARFM: 0.0217 vs. FSDAF: 0.0224 vs. LR: 0.0221). Furthermore, the lightweight algorithm design and calculations based on the Google Earth Engine make the proposed method computationally less expensive than the STARFM and FSDAF.

scholarly journals An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 611 ◽  
Author(s):  
Zhe Wang ◽  
Fuyuan Xiao
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Weighted Average ◽  
Weights Of Evidence ◽  
Experimental Result ◽  
Fusion Method ◽  
Combination Rule ◽  
Combination Rules ◽  
Source Data ◽  
Belief Entropy

Dempster–Shafer (DS) evidence theory is widely applied in multi-source data fusion technology. However, classical DS combination rule fails to deal with the situation when evidence is highly in conflict. To address this problem, a novel multi-source data fusion method is proposed in this paper. The main steps of the proposed method are presented as follows. Firstly, the credibility weight of each piece of evidence is obtained after transforming the belief Jenson–Shannon divergence into belief similarities. Next, the belief entropy of each piece of evidence is calculated and the information volume weights of evidence are generated. Then, both credibility weights and information volume weights of evidence are unified to generate the final weight of each piece of evidence before the weighted average evidence is calculated. Then, the classical DS combination rule is used multiple times on the modified evidence to generate the fusing results. A numerical example compares the fusing result of the proposed method with that of other existing combination rules. Further, a practical application of fault diagnosis is presented to illustrate the plausibility and efficiency of the proposed method. The experimental result shows that the targeted type of fault is recognized most accurately by the proposed method in comparing with other combination rules.

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