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Open AccessArticle

Regression Tree CNN for Estimation of Ground Sampling Distance Based on Floating-Point Representation

School of Electrical Engineering, Korea University, Anam-dong, Seoul 136-713, Korea
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Remote Sens. 2019, 11(19), 2276; https://doi.org/10.3390/rs11192276
Received: 21 August 2019 / Revised: 22 September 2019 / Accepted: 27 September 2019 / Published: 29 September 2019
(This article belongs to the Section Remote Sensing Image Processing)
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. View Full-Text
Keywords: floating-point representation; binomial tree; tree CNN; regression tree; GSD estimation; aerial image; satellite image; spatial resolution floating-point representation; binomial tree; tree CNN; regression tree; GSD estimation; aerial image; satellite image; spatial resolution
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Lee, J.-H.; Sull, S. Regression Tree CNN for Estimation of Ground Sampling Distance Based on Floating-Point Representation. Remote Sens. 2019, 11, 2276.

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