  As Super-Resolution (SR) has matured as a research topic, it has been applied
to additional topics beyond image reconstruction. In particular, combining
classification or object detection tasks with a super-resolution preprocessing
stage has yielded improvements in accuracy especially with objects that are
small relative to the scene. While SR has shown promise, a study comparing SR
and naive upscaling methods such as Nearest Neighbors (NN) interpolation when
applied as a preprocessing step for object detection has not been performed. We
apply the topic to satellite data and compare the Multi-scale Deep
Super-Resolution (MDSR) system to NN on the xView challenge dataset. To do so,
we propose a pipeline for processing satellite data that combines multi-stage
image tiling and upscaling, the YOLOv2 object detection architecture, and label
stitching. We compare the effects of training models using an upscaling factor
of 4, upscaling images from 30cm Ground Sample Distance (GSD) to an effective
GSD of 7.5cm. Upscaling by this factor significantly improves detection
results, increasing Average Precision (AP) of a generalized vehicle class by 23
percent. We demonstrate that while SR produces upscaled images that are more
visually pleasing than their NN counterparts, object detection networks see
little difference in accuracy with images upsampled using NN obtaining nearly
identical results to the MDSRx4 enhanced images with a difference of 0.0002 AP
between the two methods.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing;Machine Learning