  Deep learning based object detection has achieved great success. However,
these supervised learning methods are data-hungry and time-consuming. This
restriction makes them unsuitable for limited data and urgent tasks, especially
in the applications of remote sensing. Inspired by the ability of humans to
quickly learn new visual concepts from very few examples, we propose a
training-free, one-shot geospatial object detection framework for remote
sensing images. It consists of (1) a feature extractor with remote sensing
domain knowledge, (2) a multi-level feature fusion method, (3) a novel
similarity metric method, and (4) a 2-stage object detection pipeline.
Experiments on sewage treatment plant and airport detections show that proposed
method has achieved a certain effect. Our method can serve as a baseline for
training-free, one-shot geospatial object detection.
