LSKNet: A Foundation Lightweight Backbone for Remote Sensing
This addresses challenges in remote sensing tasks for applications like environmental monitoring, though it is incremental as it adapts existing kernel mechanisms to a new domain.
The paper tackled the problem of remote sensing image analysis by proposing LSKNet, a lightweight backbone that dynamically adjusts its receptive field to model long-range context, achieving new state-of-the-art scores on classification, object detection, and semantic segmentation benchmarks.
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.