Abstract
Remote sensing scene classification plays a key role in Earth observation by enabling the automatic identification of land use and land cover (LULC) patterns from aerial and satellite imagery. Despite recent progress with convolutional neural networks (CNNs) and vision transformers (ViTs), the task remains challenging due to variations in spatial resolution, viewpoint, orientation, and background conditions, which often reduce the generalization ability of existing models. To address these challenges, this paper proposes a lightweight architecture based on the convolutional mixer paradigm. The model alternates between spatial mixing through depthwise convolutions at multiple scales and channel mixing through pointwise operations, enabling efficient extraction of both local and contextual information while keeping the number of parameters and computations low. Extensive experiments were conducted on the AID and EuroSAT benchmarks. The proposed model achieved overall accuracy, average accuracy, and Kappa values of 74.7%, 74.57%, and 73.79 on the AID dataset, and 93.90%, 93.93%, and 93.22 on EuroSAT, respectively. These results demonstrate that the proposed approach provides a good balance between accuracy and efficiency compared with widely used CNN- and transformer-based models. Code will be publicly available on: this https URL
Abstract (translated)
遥感场景分类在地球观测中扮演着关键角色，它能够通过分析航空和卫星图像自动识别土地利用与覆盖（LULC）模式。尽管近期基于卷积神经网络（CNNs）和视觉变换器（ViTs）的进步显著提升了性能，但由于空间分辨率、视角、方向以及背景条件的差异导致现有模型泛化能力不足，这一任务仍然具有挑战性。为解决这些问题，本文提出了一种基于卷积混合器范式的轻量级架构。该模型通过在多个尺度上交替使用深度可分离卷积进行空间混合和逐点操作进行通道混合，从而能够高效地提取局部及上下文信息，并保持参数数量和计算需求的低水平。 研究团队在AID（Aerial Image Dataset）和EuroSAT基准数据集上进行了广泛实验。所提出模型在AID数据集中分别达到了74.7%的整体精度、74.57%的平均准确率以及73.79的Kappa值，在EuroSAT数据集中则分别为93.90%、93.93%和93.22。这些结果表明，所提出的方法在准确性与效率之间取得了良好的平衡，相比广泛使用的基于CNNs和transformers的模型具有优势。 代码将在以下链接公开发布：[此URL](this https URL)
URL
https://arxiv.org/abs/2512.06877
