An Empirical Study of Methods for Small Object Detection from Satellite Imagery

TL;DR

This paper empirically evaluates four state-of-the-art small object detection methods on satellite imagery, focusing on car and bee box detection, to understand their performance and challenges in remote sensing applications.

Contribution

It provides a comparative analysis of leading detection methods on satellite datasets, highlighting their strengths and limitations for small object detection.

Findings

Identifies top-performing methods for small object detection in satellite images

Provides insights into technical challenges faced by current methods

Evaluates methods using high-resolution satellite datasets

Abstract

This paper reviews object detection methods for finding small objects from remote sensing imagery and provides an empirical evaluation of four state-of-the-art methods to gain insights into method performance and technical challenges. In particular, we use car detection from urban satellite images and bee box detection from satellite images of agricultural lands as application scenarios. Drawing from the existing surveys and literature, we identify several top-performing methods for the empirical study. Public, high-resolution satellite image datasets are used in our experiments.