Deepspace-scalenet for small celestial body exploration

HIGHLIGHTS

• who: Remote Sens. et al. from the National Space Science Centre, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China have published the article: DeepSpace-ScaleNet for Small Celestial Body Exploration, in the Journal: (JOURNAL) of 23/Oct/2022
• what: To tackle the problems mentioned in the abstract, the authors propose DeepSpace Scalenet, which fills the gap of scale estimation for deep space scenes. The experiments demonstrate that it is an effective and efficient module to capture the relationship between correlation maps. The authors compare DeepSpace-ScaleNet with other state . . .
  

   

  

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