# Towards Lightweight and Multi-Scale Scene Classification: A Lie Group-Guided Deep Learning Network with Collaborative Attention

## TL;DR

This paper introduces a lightweight deep learning network for remote sensing scene classification that improves accuracy by combining shallow and high-level features.

## Contribution

The novel LGLMNet integrates Lie Group covariance features and a dual-branch architecture with a collaborative attention mechanism.

## Key findings

- LGLMNet improves accuracy by 2.14% on UCM-21, 2.32% on AID, and 1.12% on NWPU-45 datasets.
- The network maintains a lightweight structure with only 2.6 million parameters.
- A cross-layer fusion block effectively merges shallow and high-level features.

## Abstract

Remote sensing scene classification (RSSC) plays a crucial role in Earth observation. Current deep learning methods, while accurate, tend to focus on high-level semantic features and overlook complementary shallow details such as edges and textures. Moreover, conventional CNNs are limited by fixed receptive fields, whereas transformers incur high computational costs. To address these limitations, we propose the Lie Group lightweight multi-scale network (LGLMNet), a lightweight multi-scale network that integrates Lie Group covariance features. It employs a dual-branch architecture combining Lie Group machine learning (LGML) for shallow feature extraction and a deep learning branch for high-level semantics. In the deep branch, we design a parallel depthwise separable convolution block (PDSCB) for multi-scale perception and a spatial-channel collaborative attention mechanism (SCCA) for efficient global–local modeling. A cross-layer feature fusion block (CLFFB) effectively merges the two branches. Compared with state-of-the-art methods, the proposed LGLMNet achieves accuracy improvements of 2.14%, 2.32%, and 1.12% on UCM-21, AID, and NWPU-45 datasets, respectively, while maintaining a lightweight structure with only 2.6 M parameters.