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Advances in multi-sensor data fusion: algorithms and applications
Jiang Dong1, Dafang Zhuang1, Yaohuan Huang1
1Data Center for Resources and Environmental Sciences, State Key Lab of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Sensors (Basel, Switzerland)
|March 13, 2012
Summary
Multi-sensor satellite image fusion combines data from various sensors to enhance remote sensing applications. This overview highlights recent algorithm improvements and application advances, recommending future research directions.
Area of Science:
- Remote Sensing
- Image Processing
- Geospatial Analysis
Background:
- Increasing availability of multi-sensor satellite and remote sensing data.
- Image fusion is crucial for extracting more information than from single sensors.
- A growing research area since the late 20th century.
Purpose of the Study:
- To provide an overview of recent advancements in multi-sensor satellite image fusion.
- To introduce popular fusion algorithms and their improvements.
- To describe progress in key remote sensing application fields.
Main Methods:
- Review of existing multi-sensor satellite image fusion algorithms.
- Analysis of recent improvements in fusion techniques.
- Examination of applications in object identification, classification, change detection, and target tracking.
Main Results:
- Popular fusion algorithms and their recent enhancements are detailed.
- Significant advances in remote sensing applications using fused imagery are presented.
- Advantages and limitations of current application methods are discussed.
Conclusions:
- Need for improved fusion algorithms.
- Development of "algorithm fusion" methodologies is recommended.
- Establishment of automatic quality assessment schemes for fused images is advised.