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, semantic segmentation and change detection, 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 backbone network sets new state-of-the-art scores on standard remote sensing classification, object detection, semantic segmentation and change detection 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.

遥感图像由于其固有的复杂性，给下游任务带来了明显的挑战。虽然大量研究致力于遥感分类、目​​标检测、语义分割和变化检测，但大多数研究都忽略了遥感场景中嵌入的有价值的先验知识。这种先验知识可能很有用，因为在没有参考足够长范围的背景的情况下，可能会错误地识别遥感对象，而不同的对象可能会有所不同。本文考虑了这些先验，并提出了一种轻量级大型选择性核网络（LSKNet）主干网。 LSKNet可以动态调整其大的空间感受野，以更好地模拟遥感场景中各种物体的测距上下文。据我们所知，此前尚未在遥感图像中探索过大型且选择性的核机制。我们的轻量级 LSKNet 主干网络没有任何附加功能，在标准遥感分类、对象检测、语义分割和变化检测基准上设置了新的最先进分数。我们的综合分析进一步验证了所识别先验的重要性和 LSKNet 的有效性。代码可在 https://github.com/zcablii/LSKNet 获取。