  We present the RSSOD-Bench dataset for salient object detection (SOD) in
optical remote sensing imagery. While SOD has achieved success in natural scene
images with deep learning, research in SOD for remote sensing imagery (RSSOD)
is still in its early stages. Existing RSSOD datasets have limitations in terms
of scale, and scene categories, which make them misaligned with real-world
applications. To address these shortcomings, we construct the RSSOD-Bench
dataset, which contains images from four different cities in the USA. The
dataset provides annotations for various salient object categories, such as
buildings, lakes, rivers, highways, bridges, aircraft, ships, athletic fields,
and more. The salient objects in RSSOD-Bench exhibit large-scale variations,
cluttered backgrounds, and different seasons. Unlike existing datasets,
RSSOD-Bench offers uniform distribution across scene categories. We benchmark
23 different state-of-the-art approaches from both the computer vision and
remote sensing communities. Experimental results demonstrate that more research
efforts are required for the RSSOD task.
