Segmentation of Extreme Ultraviolet Solar Images using a Multispectral Data Fusion Process

2007 ◽  
Author(s):  
Vincent Barra ◽  
Veronique Delouille ◽  
Jean-Francois Hochedez
Keyword(s):  
Data Fusion ◽  
Extreme Ultraviolet ◽  
Fusion Process ◽  
Multispectral Data

Development for granular computing-based multi-agent system for data fusion process

2013 ◽  
Vol 48 (4) ◽  
pp. 321
Author(s):  
Bin Ma ◽  
Nannan Li ◽  
Kuan Huang ◽  
Changtao Wang ◽  
Zhonghua Han ◽  
...  
Keyword(s):  
Data Fusion ◽  
Granular Computing ◽  
Fusion Process ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

Dynamic Localization of Car-like Vehicles using Data Fusion of Redundant ABS Sensors

2003 ◽  
Vol 56 (3) ◽  
pp. 429-441 ◽  
Author(s):  
Ph. Bonnifait ◽  
P. Bouron ◽  
D. Meizel ◽  
P. Crubillé
Keyword(s):  
Data Fusion ◽  
Fusion Process ◽  
Dynamic Localization ◽  
Localization System ◽  
Gps Measurement ◽  
Driving Wheel ◽  
Noticeable Reduction ◽  
Using Data ◽  
Better Than

A localization system using GPS, ABS sensors and a driving wheel encoder is described and tested through real experiments. A new odometric technique using the four ABS sensors is presented. Due to the redundancy of measurements, the precision is better than the method using differential odometry on the rear wheels only. The sampling is performed when necessary and when a GPS measurement is performed. This implies a noticeable reduction of the GPS latency, simplifying the data-fusion process and improving the quality of its results.

Robust Coupled Non-Negative Matrix Factorization for Hyperspectral and Multispectral Data Fusion

2021 ◽  
Author(s):  
Touseef Ahmad ◽  
Rosly B Lyngdoh ◽  
S S Anand ◽  
Praveen K Gupta ◽  
Arundhati Misra ◽  
...  
Keyword(s):  
Data Fusion ◽  
Matrix Factorization ◽  
Multispectral Data ◽  
Non Negative Matrix Factorization

scholarly journals Multisource Data Fusion Framework for Land Use/Land Cover Classification Using Machine Vision

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Salman Qadri ◽  
Dost Muhammad Khan ◽  
Syed Furqan Qadri ◽  
Abdul Razzaq ◽  
Nazir Ahmad ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Machine Vision ◽  
Data Fusion ◽  
Spectral Feature ◽  
Texture Feature ◽  
Texture Features ◽  
Features Selection ◽  
Land Use Land Cover ◽  
Multispectral Data

Data fusion is a powerful tool for the merging of multiple sources of information to produce a better output as compared to individual source. This study describes the data fusion of five land use/cover types, that is, bare land, fertile cultivated land, desert rangeland, green pasture, and Sutlej basin river land derived from remote sensing. A novel framework for multispectral and texture feature based data fusion is designed to identify the land use/land cover data types correctly. Multispectral data is obtained using a multispectral radiometer, while digital camera is used for image dataset. It has been observed that each image contained 229 texture features, while 30 optimized texture features data for each image has been obtained by joining together three features selection techniques, that is, Fisher, Probability of Error plus Average Correlation, and Mutual Information. This 30-optimized-texture-feature dataset is merged with five-spectral-feature dataset to build the fused dataset. A comparison is performed among texture, multispectral, and fused dataset using machine vision classifiers. It has been observed that fused dataset outperformed individually both datasets. The overall accuracy acquired using multilayer perceptron for texture data, multispectral data, and fused data was 96.67%, 97.60%, and 99.60%, respectively.

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