Multi-source remote sensing data fusion: status and trends

By Jixian Zhang. 2010

772 citations recorded by the source corpus.

Subfields: Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/92593979bd152eadb3d316d68e74327a0adbc087

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. B. Cooke
2. Rob S. Skakun
3. Rafael Molina
4. R. Hall
5. Guillermo Castilla
6. Aggelos K. Katsaggelos
7. Matteo Matteucci
8. Sara Comai

In Media Technology

1. Leyuan Fang
2. Shutao Li
3. Xudong Kang
4. Haitao Yin
5. Jianwen Hu
6. Yu Liu
7. B. Cooke
8. Rob S. Skakun

Citations in the corpus

• A survey of classical methods and new trends in pansharpening of multispectral images (2011, background): “different levels in order to derive spatial details that are imported into finer scales of the MS images, highlight the relationship between PAN and MS images in coarser scales and enhance spatial details [87].”
• Pixel-level image fusion: A survey of the state of the art (2017, background): “Jixiang Zhang reviews current techniques of multi-source remote sensing data fusion (including pixel, feature, and decision level fusion) and discusses their future trends and challenges [9].”
• Advances in fusion of optical imagery and LiDAR point cloud applied to photogrammetry and remote sensing (2017, neutral): “In addition, Rottensteiner (2009) focused on building and road extraction from fused data, Zhang (2010) emphasised the importance of information extraction from the fused data, and Wu and Tang (2015) focused on the geometric fusion of LiDAR data and images.”
• Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data (2017, neutral): “2681128 multisources data fusion techniques [4], [5].”
• Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective* (2016, background): “The second area, image fusion, involves the integration of multi-source, multi-resolution information to improve the change detection process (Zhang 2010; Du et al. 2013).”
• UAV & satellite synergies for optical remote sensing applications: A literature review (2021, neutral): “In general, fusion techniques in RS can be classi fi ed into three categories according to the degree of fusion (Zhang, 2010): pixel-level, feature-level and decision-level.”
• A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, methodology): “A large number of data fusion techniques have been developed, as reviewed by Pohl and van Genderen (1998), Li (2010), Zhang (2010), and Khaleghi et al. (2013).”
• Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, background): “Data fusion is the process of combining data from multiple sources to improve the potential values and interpretation performance of the source data, and to produce a high-quality visible representation of the data [152].”