Satellite Imagery Multiscale Rapid Detection with Windowed Networks

TL;DR

This paper introduces SIMRDWN, a fast, scalable pipeline for detecting small objects in large satellite images using multiple detection frameworks, achieving high accuracy and speed.

Contribution

The paper presents a unified, multiscale detection pipeline for satellite imagery that integrates various models and enables rapid, high-resolution object detection over large areas.

Findings

YOLT achieves highest mAP and fastest inference speed.

Using two detectors at different scales improves detection accuracy.

The pipeline processes images at native resolution at > 0.2 km²/sec.

Abstract

Detecting small objects over large areas remains a significant challenge in satellite imagery analytics. Among the challenges is the sheer number of pixels and geographical extent per image: a single DigitalGlobe satellite image encompasses over 64 km2 and over 250 million pixels. Another challenge is that objects of interest are often minuscule (~pixels in extent even for the highest resolution imagery), which complicates traditional computer vision techniques. To address these issues, we propose a pipeline (SIMRDWN) that evaluates satellite images of arbitrarily large size at native resolution at a rate of > 0.2 km2/s. Building upon the tensorflow object detection API paper, this pipeline offers a unified approach to multiple object detection frameworks that can run inference on images of arbitrary size. The SIMRDWN pipeline includes a modified version of YOLO (known as YOLT), along with the models of the tensorflow object detection API: SSD, Faster R-CNN, and R-FCN. The proposed approach allows comparison of the performance of these four frameworks, and can rapidly detect objects of vastly different scales with relatively little training data over multiple sensors. For objects of very different scales (e.g. airplanes versus airports) we find that using two different detectors at different scales is very effective with negligible runtime cost.We evaluate large test images at native resolution and find mAP scores of 0.2 to 0.8 for vehicle localization, with the YOLT architecture achieving both the highest mAP and fastest inference speed.