An Empirical Study of Methods for Small Object Detection from Satellite Imagery
It addresses the problem of small object detection in remote sensing for applications like urban monitoring and agriculture, but is incremental as it reviews and tests existing methods.
This paper empirically evaluates four state-of-the-art object detection methods for small objects in satellite imagery, focusing on car detection in urban areas and bee box detection in agricultural lands, using public high-resolution datasets to assess performance and challenges.
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.