  Remote sensing images pose distinct challenges for downstream tasks due to
their inherent complexity. While a considerable amount of research has been
dedicated to remote sensing classification, object detection and semantic
segmentation, most of these studies have overlooked the valuable prior
knowledge embedded within remote sensing scenarios. Such prior knowledge can be
useful because remote sensing objects may be mistakenly recognized without
referencing a sufficiently long-range context, which can vary for different
objects. This paper considers these priors and proposes a lightweight Large
Selective Kernel Network (LSKNet) backbone. LSKNet can dynamically adjust its
large spatial receptive field to better model the ranging context of various
objects in remote sensing scenarios. To our knowledge, large and selective
kernel mechanisms have not been previously explored in remote sensing images.
Without bells and whistles, our lightweight LSKNet sets new state-of-the-art
scores on standard remote sensing classification, object detection and semantic
segmentation benchmarks. Our comprehensive analysis further validated the
significance of the identified priors and the effectiveness of LSKNet. The code
is available at https://github.com/zcablii/LSKNet.
