Terrain classification using static and dynamic texture features by UAV downwash effect

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
18 Downloads (Pure)


knowing how to identify terrain types is especially important in the autonomous navigation, mapping, decision making and emergency landings areas. For example, an unmanned aerial vehicle (UAV) can use it to find a suitable landing position or to cooperate with other robots to navigate across an unknown region. Previous works on terrain classification from RGB images taken onboard of UAVs shown that only static pixel-based features were tested with a considerable classification error. This paper presents a computer vision algorithm capable of identifying the terrain from RGB images with improved accuracy. The algorithm complement the static image features and dynamic texture patterns produced by UAVs rotors downwash effect (visible at lower altitudes) and machine learning methods to classify the underlying terrain. The system is validated using videos acquired onboard of a UAV with a RGB camera.

Original languageEnglish
Pages (from-to)84-93
Number of pages10
JournalJournal of Automation, Mobile Robotics and Intelligent Systems
Issue number1
Publication statusPublished - 7 Feb 2019


  • Image processing
  • Machine Learning
  • Neural networks
  • Terrain classification
  • Texture
  • UAV


Dive into the research topics of 'Terrain classification using static and dynamic texture features by UAV downwash effect'. Together they form a unique fingerprint.

Cite this