Inverted Residual Bottlenecks with Large Kernel Attention for Remote Scene Classification

Remote sensing image classification plays a piv-otal role in environmental monitoring and urban planning, yet it faces the challenge of accurately interpreting complex and high-resolution images with fast inference speed for real time applications. To address this, we introduce the Mobile Large Kernel Attention Network (MLKANet), which integrates MobileNetV2's inverted residual structures with the large kernel attention mechanism from the Visual Attention Network (VAN). Our proposed MLKANet achieves a compelling balance of computational efficiency and sophisticated feature extraction, while maintaining the speed from the MobileNetV2 baseline. This study evaluates MLKANet's performance against state-of-the-art models using the Aerial Image Dataset (AID), demonstrating superior accuracy and efficiency. The architecture's effectiveness is further evidenced through an ablation study highlighting the scalability of our approach and class-wise performance analysis that showcases MLKANet's proficiency across various scene types.