Spatial Dimension Reduction
Spatial Dimension Reduction is a statistical methodology that reduces the number of spatial variables or coordinates in a dataset while preserving essential spatial structure and dependencies, distinguishing it from non-spatial dimension reduction techniques by explicitly accounting for spatial autocorrelation and geographic relationships.
595
Publications
37.1K
Citations
2.1K
Authors
852
Institutions
Publications per year
2017–2026
272
Parents
595
GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing
Xiaohong Liu, Yongrui Ma, Zhihao Shi et al. · 2019 · 1K citations
1–5 of 595
2.1K
6
h-index
6
h-index
5
h-index
4
h-index
4
h-index
1–5 of 2.1K
852

Chinese Academy of Sciences
Beijing, China
12 h-index
29 publications
1.5K citations

Shanghai Jiao Tong University
Shanghai, China
10 h-index
27 publications
827 citations

Centre National de la Recherche Scientifique
Paris, France
10 h-index
26 publications
601 citations

Xidian University
Xi'an, China
9 h-index
31 publications
1.3K citations
Wuhan University
Wuhan, China
9 h-index
31 publications
3.9K citations
University of California, Los Angeles
Los Angeles, United States
9 h-index
17 publications
2.1K citations

University of Minnesota
Minneapolis, United States
9 h-index
19 publications
1.7K citations
Tsinghua University
Beijing, China
8 h-index
19 publications
2.1K citations
1–8 of 852
14 publications · 1.6K citations
14
h-index
11 publications · 462 citations
11
h-index
Repository · 10 publications · 422 citations
10
h-index
9 publications · 291 citations
9
h-index
Repository · 6 publications · 110 citations
6
h-index
1–5