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Airports represent essential infrastructure, offering substantial research and application potential. However, extracting airports from complex Synthetic Aperture Radar (SAR) scenes is challenging due to the cluttered background and fine structure of airports. This necessitates the integration of global and local information for fine-grained extraction. To tackle this issue, this paper introduces a novel framework for fine-grained extraction of airports from large-scale SAR images. First, a CNN-Transformer hybrid semantic segmentation network with multi-scale contextual fusion is proposed, named CTMANet. In this network, the encoder combines CNNs and Transformers to capture local and global information, while the Multi-Scale Context Aggregation (MCA) block fuses multi-scale contextual information. Skip connections between the encoder and decoder are established to minimize the loss of detailed information and fuse low-level features with high-level semantic features. Moreover, a Category Balance block is designed to address class imbalance. Experimental results on the GF-3 dataset demonstrate that CTMANet outperforms state-of-the-art methods, proving its superior suitability for fine-grained airport extraction in large-scale scenarios.

CTMANet: A CNN-Transformer Hybrid Semantic Segmentation Network for Fine-Grained Airport Extraction in Complex SAR Scenes