An optimum data fusion algorithm for distributed detection system

2002 ◽  
Author(s):  
Wang Jinsong ◽  
Wang Qi ◽  
Wang Pin ◽  
Chi Xiaozhu
Keyword(s):  
Data Fusion ◽  
Detection System ◽  
Distributed Detection ◽  
Fusion Algorithm

Application of multi-resolution data fusion algorithm

2010 ◽  
Vol 30 (9) ◽  
pp. 2556-2558 ◽  
Author(s):  
Ming-bo SHI ◽  
Ji-hong CHEN ◽  
Zheng-zheng JIANG
Keyword(s):  
Data Fusion ◽  
Fusion Algorithm ◽  
Resolution Data

scholarly journals Phenological Analysis of Sub-Alpine Forest on Jeju Island, South Korea, Using Data Fusion of Landsat and MODIS Products

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 286
Author(s):  
Sang-Jin Park ◽  
Seung-Gyu Jeong ◽  
Yong Park ◽  
Sang-hyuk Kim ◽  
Dong-kun Lee ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Data Fusion ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Study Region ◽  
Modis Imagery ◽  
Increasing Temperature

Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R2 = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.

scholarly journals An efficient intelligent data fusion algorithm for wireless sensor network

2021 ◽  
Vol 183 ◽  
pp. 418-424
Author(s):  
Haitao Wang ◽  
Lihua Song ◽  
Jue Liu ◽  
Tingting Xiang
Keyword(s):  
Wireless Sensor Network ◽  
Data Fusion ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Fusion Algorithm
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