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.

翻译：遥感图像因其固有的复杂性，为下游任务带来了独特的挑战。尽管已有大量研究致力于遥感图像分类、目标检测和语义分割，但大多数研究忽视了遥感场景中蕴含的宝贵先验知识。此类先验知识具有重要意义，因为若不参考足够长程的上下文信息，遥感目标可能被错误识别，而不同目标所需的上下文范围存在差异。本文充分考虑这些先验知识，提出了一种轻量化的大尺度选择性核网络（LSKNet）骨干架构。LSKNet能够动态调整其大空间感受野，以更好地建模遥感场景中各类目标所需的上下文范围。据我们所知，大尺度选择性核机制此前尚未在遥感图像领域得到探索。在不引入任何附加技巧的情况下，我们的轻量化LSKNet在标准遥感分类、目标检测和语义分割基准测试中均取得了最先进的性能。全面的分析进一步验证了所识别先验知识的重要性以及LSKNet的有效性。代码已开源：https://github.com/zcablii/LSKNet。