# The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery

arXiv: https://arxiv.org/abs/1812.04098 · HTML: https://mlsanity.com/paper/1812.04098/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2018.

## Built on by (most cited first, 30 of 58)

- [Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning](https://mlsanity.com/paper/2212.14532/) (2022)
- [DiffusionSat: A Generative Foundation Model for Satellite Imagery](https://mlsanity.com/paper/2312.03606/) (2023)
- [The Multi-Temporal Urban Development SpaceNet Dataset](https://mlsanity.com/paper/2102.04420/) (2021)
- [Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark](https://mlsanity.com/paper/2206.13996/) (2022)
- [SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery](https://mlsanity.com/paper/2209.13351/) (2022)
- [Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient Convolution](https://mlsanity.com/paper/2405.07023/) (2024)
- [Physical Adversarial Attacks on an Aerial Imagery Object Detector](https://mlsanity.com/paper/2108.11765/) (2021)
- [A Comprehensive Review of Deep Learning-based Single Image Super-resolution](https://mlsanity.com/paper/2102.09351/) (2021)
- [Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis](https://mlsanity.com/paper/2106.11485/) (2021)
- [Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation](https://mlsanity.com/paper/2111.03231/) (2021)
- [Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss](https://mlsanity.com/paper/2404.01692/) (2024)
- [Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art](https://mlsanity.com/paper/2111.03260/) (2021)
- [Visual and Object Geo-localization: A Comprehensive Survey](https://mlsanity.com/paper/2112.15202/) (2021)
- [A heterogeneous group CNN for image super-resolution](https://mlsanity.com/paper/2209.12406/) (2022)
- [Exploiting Diffusion Prior for Task-driven Image Restoration](https://mlsanity.com/paper/2507.22459/) (2025)
- [Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection](https://mlsanity.com/paper/2208.03062/) (2022)
- [Automated Aerial Animal Detection When Spatial Resolution Conditions Are Varied](https://mlsanity.com/paper/2110.01329/) (2021)
- [Semantically Accurate Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/2205.08659/) (2022)
- [Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery](https://mlsanity.com/paper/2303.05879/) (2023)
- [Boundless: Generating Photorealistic Synthetic Data for Object Detection in Urban Streetscapes](https://mlsanity.com/paper/2409.03022/) (2024)
- [Artificial and beneficial -- Exploiting artificial images for aerial vehicle detection](https://mlsanity.com/paper/2104.03054/) (2021)
- [Learning to Select Camera Views: Efficient Multiview Understanding at Few Glances](https://mlsanity.com/paper/2303.06145/) (2023)
- [Rethinking Intersection Over Union for Small Object Detection in Few-Shot Regime](https://mlsanity.com/paper/2307.09562/) (2023)
- [Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning](https://mlsanity.com/paper/2309.01656/) (2023)
- [Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution](https://mlsanity.com/paper/2503.04385/) (2025)
- [Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal Surveillance](https://mlsanity.com/paper/2111.06830/) (2021)
- [Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution](https://mlsanity.com/paper/2204.01736/) (2022)
- [Constellation Dataset: Benchmarking High-Altitude Object Detection for an Urban Intersection](https://mlsanity.com/paper/2404.16944/) (2024)
- [Scale-Aware Recognition in Satellite Images under Resource Constraints](https://mlsanity.com/paper/2411.00210/) (2024)
- [F2IDiff: Real-world Image Super-resolution using Feature to Image Diffusion Foundation Model](https://mlsanity.com/paper/2512.24473/) (2025)

## Abstract (arXiv)

We explore the application of super-resolution techniques to satellite imagery, and the effects of these techniques on object detection algorithm performance. Specifically, we enhance satellite imagery beyond its native resolution, and test if we can identify various types of vehicles, planes, and boats with greater accuracy than native resolution. Using the Very Deep Super-Resolution (VDSR) framework and a custom Random Forest Super-Resolution (RFSR) framework we generate enhancement levels of 2x, 4x, and 8x over five distinct resolutions ranging from 30 cm to 4.8 meters. Using both native and super-resolved data, we then train several custom detection models using the SIMRDWN object detection framework. SIMRDWN combines a number of popular object detection algorithms (e.g. SSD, YOLO) into a unified framework that is designed to rapidly detect objects in large satellite images. This approach allows us to quantify the effects of super-resolution techniques on object detection performance across multiple classes and resolutions. We also quantify the performance of object detection as a function of native resolution and object pixel size. For our test set we note that performance degrades from mean average precision (mAP) = 0.53 at 30 cm resolution, down to mAP = 0.11 at 4.8 m resolution. Super-resolving native 30 cm imagery to 15 cm yields the greatest benefit; a 13-36% improvement in mAP. Super-resolution is less beneficial at coarser resolutions, though still provides a small improvement in performance.