Abstract
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 language | English |
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Article number | 2276 |
Journal | Remote Sensing |
Volume | 11 |
Issue number | 19 |
DOIs | |
Publication status | Published - 2019 Oct 1 |
Keywords
- Aerial image
- Binomial tree
- Floating-point representation
- GSD estimation
- Regression tree
- Satellite image
- Spatial resolution
- Tree CNN
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)