Aircraft Classification via Dual-Branch Color–Shape Feature Learning and Cross-Attention Fusion
Xianyun Qian, Pi Liu · Applied Sciences · 2026
Aircraft type classification plays a crucial role in various applications, including remote sensing, surveillance, and aviation management. Since the development of deep learning techniques, nearly all related methods are based on neural networks, achieving excellent classification results. However, existing classification networks primarily focus on optimizing single-branch architectures, often overlooking the underlying factors driving recognition performance. Our analysis suggests that color and shape are two important and complementary visual cues for aircraft classification, with their relative importance varying across datasets and imaging scenarios. Motivated by this insight, we propose a novel dual-branch network architecture that separately processes shape and color cues, allowing each branch to emphasize one type of visual information before adaptive fusion. Specifically, we designed two dedicated modules: a Shape Feature Module (SFM) and a Color Feature Module (CFM), tailored for extracting shape and color information independently. Furthermore, we introduced a Color–Shape Cross-Attention-based Fusion Module (CSCAFM) to integrate these features. Within CSCAFM, the separated shape and color features are adaptively fused through a cross-attention mechanism, enabling the network to dynamically weigh the contributions of shape and color. Experimental results on benchmark datasets demonstrate the effectiveness of our approach.