CFMNet: A Lightweight Backbone With Cooperative Feature Modeling for Remote Sensing Vision Tasks

Abstract

High-resolution remote sensing imagery presents unique challenges for efficient visual understanding, including dense object distributions, severe scale variations, strong background redundancy, and complex spatial structures. Existing deep models often rely on deep architectures or computationally intensive global modeling strategies, limiting their deployment on resource-constrained platforms. In this article, we propose an efficient and lightweight backbone network, termed the cooperative feature modeling network (CFMNet), for high-resolution remote sensing image understanding. CFMNet decomposes feature representations into heterogeneous yet complementary subspaces and models them cooperatively within a unified framework. Specifically, it coordinates channel semantics, structure-aware spatial dependencies, local detail enhancement, and global contextual consistency to improve representation efficiency while suppressing redundant computation. Extensive experiments demonstrate the effectiveness and generality of CFMNet. It achieves 96.13%, 95.50%, and 98.10% Top-1 accuracy on NWPU-RESISC45, aerial image dataset (AID), and UC Merced Land Use dataset (UCM), respectively, 79.82% mAP on DOTA-v1.0, 73.02% mAP on DOTA-v1.5, and 90.82% mAP on HRSC2016, as well as 83.8% mIoU on Vaihingen and 53.8% mIoU on LoveDA, while maintaining low parameter count and computational complexity. A scaling-based Pareto analysis on DOTA-v1.0 and LoveDA further shows that CFMNet variants form a favorable efficiency–accuracy frontier compared with representative lightweight backbones. These results indicate that cooperative modeling of heterogeneous features provides an effective and efficient solution for high-resolution remote sensing image understanding. The code will be released at https://github.com/BEIBEIPRINCESS/CFMNet