Remote sensing image classification algorithm based on texture feature and extreme learning machine

Xiangchun Liu, Jing Yu, Wei Song, Xinping Zhang, Lizhi Zhao, Antai Wang

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


With the development of satellite technology, the satellite imagery of the earth's surface and the whole surface makes it possible to survey surface resources and master the dynamic changes of the earth with high efficiency and low consumption. As an important tool for satellite remote sensing image processing, remote sensing image classification has become a hot topic. According to the natural texture characteristics of remote sensing images, this paper combines different texture features with the Extreme Learning Machine, and proposes a new remote sensing image classification algorithm. The experimental tests are carried out through the standard test dataset SAT-4 and SAT-6. Our results show that the proposed method is a simpler and more efficient remote sensing image classification algorithm. It also achieves 99.434% recognition accuracy on SAT-4, which is 1.5% higher than the 97.95% accuracy achieved by DeepSat. At the same time, the recognition accuracy of SAT-6 reaches 99.5728%, which is 5.6% higher than DeepSat's 93.9%.

Original languageEnglish (US)
Pages (from-to)1385-1395
Number of pages11
JournalComputers, Materials and Continua
Issue number2
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Biomaterials
  • Modeling and Simulation
  • Mechanics of Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Extreme learning machine
  • Gray level co-occurrence matrix
  • Image classification


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