Estimating the Spatial Resolution of Very High-Resolution Overhead Imagery

Abstract

We investigate the problem of estimating the spatial resolution of overhead imagery. More overhead imagery is becoming available without such meta-data either because it was not collected in the first place or was not preserved with the imagery. Knowing the spatial resolution can be important for a range of automated image understanding tasks such as object detection, semantic segmentation, etc. In this paper, we explore a regression framework with a feature extraction frontend and a dilated convolution backend to estimate the spatial resolution of an overhead image. We show that a stacked auto-encoder frontend outperforms a standard convolution neural network feature extractor. In order to demonstrate our approach, we construct an evaluation dataset consisting of a large collection of very high-resolution overhead images with spatial resolutions ranging from 0.15 to 1.0 meters per pixel. CCS CONCEPTS • Computing methodologies → Scene understanding; Supervised learning by regression; Neural networks.