LSKNet: A Foundation Lightweight Backbone for Remote Sensing

TL;DR

LSKNet is a lightweight, adaptive backbone that models long-range context in remote sensing images, achieving state-of-the-art results in classification, detection, and segmentation tasks by leveraging novel large and selective kernel mechanisms.

Contribution

The paper introduces LSKNet, a novel lightweight backbone with large, selective kernels that adaptively model context in remote sensing images, a concept not previously explored in this domain.

Findings

Sets new state-of-the-art scores on remote sensing benchmarks.

Effectively models long-range context in remote sensing images.

Validated the importance of prior knowledge in remote sensing tasks.

Abstract

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.