Regression tree CNN for estimation of ground sampling distance based on floating-point representation

Jae Hun Lee, Sanghoon Sull

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


The estimation of ground sampling distance (GSD) from a remote sensing image enables measurement of the size of an object as well as more accurate segmentation in the image. In this paper, we propose a regression tree convolutional neural network (CNN) for estimating the value of GSD from an input image. The proposed regression tree CNN consists of a feature extraction CNN and a binomial tree layer. The proposed network first extracts features from an input image. Based on the extracted features, it predicts the GSD value that is represented by the floating-point number with the exponent and its mantissa. They are computed by coarse scale classification and finer scale regression, respectively, resulting in improved results. Experimental results with a Google Earth aerial image dataset and a mixed dataset consisting of eight remote sensing image public datasets with different GSDs show that the proposed network reduces the GSD prediction error rate by 25% compared to a baseline network that directly estimates the GSD.

Original languageEnglish
Article number2276
JournalRemote Sensing
Issue number19
Publication statusPublished - 2019 Oct 1

Bibliographical note

Funding Information:
Funding: This research was supported by the Agency for Defense Development (ADD) and Defense Acquisition Program Administration (DAPA) of Korea (UC160016FD).

Publisher Copyright:
© 2019 by the authors.


  • Aerial image
  • Binomial tree
  • Floating-point representation
  • GSD estimation
  • Regression tree
  • Satellite image
  • Spatial resolution
  • Tree CNN

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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