LSKNet: A Foundation Lightweight Backbone for Remote Sensing
Yuxuan Li, Xiang Li, Yimian Dai, Qibin Hou, Li Liu, Yongxiang Liu, Ming-Ming Cheng, Jian Yang

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…
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Taxonomy
TopicsGeographic Information Systems Studies
Methodsguidence~How to file a complaint against Expedia? · Softmax · Dilated Convolution · Selective Kernel Convolution · *Communicated@Fast*How Do I Communicate to Expedia? · Batch Normalization · 1x1 Convolution · Selective Kernel
