SceneMixer: Exploring Convolutional Mixing Networks for Remote Sensing Scene Classification

TL;DR

SceneMixer introduces a lightweight convolutional mixer network for remote sensing scene classification, effectively balancing accuracy and computational efficiency across challenging aerial and satellite imagery datasets.

Contribution

The paper presents a novel convolutional mixer architecture tailored for remote sensing, combining multi-scale spatial and channel mixing to improve classification performance.

Findings

Achieved 74.7% accuracy on AID dataset

Attained 93.9% accuracy on EuroSAT dataset

Demonstrated competitive efficiency compared to CNNs and ViTs

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: https://github.com/mqalkhatib/SceneMixer