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Topic · Media Technology

Remote-Sensing Image Classification

Remote-Sensing Image Classification is a research topic within Media Technology. Science Explorer counts 38k research works in it since 1955. 9.9% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the advances in hyperspectral image analysis, remote sensing, and classification. It covers topics such as deep learning, change detection, spectral unmixing, feature extraction, and object-based analysis for remote sensing applications.

  • Hyperspectral
  • Image Analysis
  • Remote Sensing
  • Classification
  • Deep Learning
  • Change Detection
  • Spectral Unmixing
  • Feature Extraction
  • Object-Based Analysis
  • Support Vector Machines
Research works
38k
fractional, since 1955
In the world top 10%
3.7k
per year above
Top-10% rate
9.9%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+37%
the tick is no change
Largest producer
China
68% of the 10 largest, 2022-now
Remote-Sensing Image Classification

Which countries lead Remote-Sensing Image Classification research?

By volume, China and India publish the most (6.8k and 1.3k works in 2022-now).

By volume, 2022-now

  1. 1 China 6.8k works
  2. 2 India 1.3k works
  3. 3 United States 645 works
  4. 4 Italy 241 works
  5. 5 Germany 207 works
  6. 6 France 188 works
  7. 7 Iran 165 works
  8. 8 United Kingdom 160 works
  9. 9 Türkiye 159 works
  10. 10 Japan 133 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 68.4%India: 12.6%United States: 6.5%Italy: 2.4%6 others listed: 10.1%68%largest
China6,832 · 68.4%India1,258 · 12.6%United States645 · 6.5%Italy241 · 2.4%6 others listed1,012 · 10.1%

Shares of the rows listed above, not of the whole node.

Which institutions lead Remote-Sensing Image Classification research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Remote-Sensing Image Classification research, followed by Xidian University and Wuhan University.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 444 works
  2. 2 Xidian UniversityChina 262 works
  3. 3 Wuhan UniversityChina 208 works
  4. 4 Northwestern Polytechnical UniversityChina 143 works
  5. 5 China University of GeosciencesChina 125 works
  6. 6 Beijing Institute of TechnologyChina 107 works
  7. 7 National University of Defense TechnologyChina 93 works
  8. 8 Nanjing University of Science and TechnologyChina 91 works
  9. 9 University of Chinese Academy of SciencesChina 85 works
  10. 10 Harbin Institute of TechnologyChina 83 works

Who are the leading researchers in Remote-Sensing Image Classification?

The most-cited researchers publishing on Remote-Sensing Image Classification include Yoshua Bengio, Simon Iain Hay and Xiangyu Zhang.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Simon Iain Hay United States 452k citations
  3. 3 Xiangyu Zhang 327k citations
  4. 4 Jian Sun 281k citations
  5. 5 Xiaogang Wang China 260k citations
  6. 6 Yann LeCun United States 256k citations
  7. 7 Andrew Zisserman United Kingdom 255k citations
  8. 8 Li Fei-Fei United States 225k citations
  9. 9 Kai You Wang China 224k citations
  10. 10 Stephen M. Smith United Kingdom 214k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Remote-Sensing Image Classification?

The most cited paper in Remote-Sensing Image Classification is "Textural Features for Image Classification" (Robert M. Haralick, K. Shanmugam, Its'Hak Dinstein, 1973), with 22,878 citations. Next come "Robust Real-Time Face Detection" (14,253) and "A Global Geometric Framework for Nonlinear Dimensionality Reduction" (13,882).

Most cited papers
#PaperReleasedCitations
1 Textural Features for Image Classification Robert M. Haralick, K. Shanmugam, Its'Hak Dinstein · IEEE Transactions on Systems Man and Cybernetics Nov 1973 22,878
2 Robust Real-Time Face Detection Paul A. Viola, Michael Jones · International Journal of Computer Vision 22 Jan 2004 14,253
3 A Global Geometric Framework for Nonlinear Dimensionality Reduction Joshua B. Tenenbaum, Vin de Silva, John C. Langford · Science 22 Dec 2000 13,882
4 Mean shift: a robust approach toward feature space analysis Dorin Comaniciu, Peter Meer · IEEE Transactions on Pattern Analysis and Machine Intelligence May 2002 11,456
5 Principal component analysis: a review and recent developments Ian T. Jolliffe, Jorge F. C. L. Cadima · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences Open access 7 Mar 2016 10,078
All 100 most cited papers, with filters and sorting

Where is Remote-Sensing Image Classification research done?

The largest centres of Remote-Sensing Image Classification research in 2022-now are Beijing (China), Xi'an (China), Wuhan (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Starkville, Fuxin and Badajoz.

Largest cities, 2022-now

  1. 1 BeijingChina 1.4k works
  2. 2 Xi'anChina 677 works
  3. 3 WuhanChina 570 works
  4. 4 NanjingChina 408 works
  5. 5 ChangshaChina 254 works
  6. 6 ShanghaiChina 226 works
  7. 7 ChengduChina 209 works
  8. 8 HarbinChina 186 works
  9. 9 GuangzhouChina 176 works
  10. 10 QingdaoChina 148 works

Where it is the local speciality

  1. StarkvilleUS · 33.7 works15×
  2. FuxinCN · 20.8 works10×
  3. BadajozES · 22.4 works8.7×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Remote-Sensing Image Classification than the world average.

See Remote-Sensing Image Classification on the map

Where is the best place to study Remote-Sensing Image Classification?

Among universities, judged by research, Wuhan University, Hunan University and China University of Geosciences score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 21.46%fractional works in this field (log) →share of work in the world top 10% →Hunan UniversityWuhan UniversityChina University of …Xidian University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Wuhan UniversityChina 78.521.4%14.2×208 +25.6%
2Hunan UniversityChina 77.727.5%7.4×54 +77.2%
3China University of GeosciencesChina 74.219.2%26.6×125 +70.8%
4Xidian UniversityChina 72.715.2%34.8×262 +58.6%
5Nanjing University of Information Science and TechnologyChina 70.222.3%15.3×64 +103.1%
6Northwestern Polytechnical UniversityChina 68.414.9%12.1×143 +34.2%
7Ningbo UniversityChina 65.324.0%5.3×34 +257.2%
8Hubei UniversityChina 64.928.7%7.6×15 +31.0%
9Beijing Institute of TechnologyChina 63.414.8%8.8×107 +141.1%
10Mississippi State UniversityUnited States 62.426.6%16.5×33 +3.0%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Remote-Sensing Image Classification, country by country

Is Remote-Sensing Image Classification research growing?

Output in 2022-2025 was 37% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Remote-Sensing Image Classification.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Remote-Sensing Image Classification as a percentage of every work published that year, 0.049% in 2024.

still counting0.020%0.040%0.060%Remote-Sensing Image Classification0.053% in 202519801990200020102020

Fractional works of Remote-Sensing Image Classification over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Remote-Sensing Image Classification9.9% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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