Building Detection From Panchromatic and Multispectral Images With Dual-Stream Asymmetric Fusion Networks
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing(2023)
摘要
Building detection from panchromatic (PAN) and multispectral (MS) images is an essential task for many practical applications. In this article, a dual-stream asymmetric fusion network is proposed, named DAFNet. DAFNet can achieve effective information fusion at the feature level. It obtains better building detection performance from the following three perspectives: a two-stream network structure is designed to guarantee the ability to extract information from PAN and MS images; an asymmetric feature fusion module is proposed to fuse features efficiently and concisely; and two consistency regularization losses, i.e., PAN information preservation loss and cross-modal semantic consistency loss are applied to further explore the consistency between features for better fusion. The experiments are conducted on a challenging building detection dataset collected from GaoFen-2 satellite images. Comprehensive evaluations on 12 popular detection methods demonstrate the superiority of our DAFNet compared with the existing state-of-the-art fusion methods. We reveal that feature-level fusion is more suitable for building detection from PAN-MS images.
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关键词
Building detection,deep learning,multimodal fusion,remote sensing (RS) images
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