Feature Pyramid Network with Multi-Head Attention for Se-mantic Segmentation of Fine-Resolution Remotely Sensed Im-ages

• URL: http://arxiv.org/abs/2102.07997v1
• Date: Tue, 16 Feb 2021 07:54:19 GMT
• Title: Feature Pyramid Network with Multi-Head Attention for Se-mantic Segmentation of Fine-Resolution Remotely Sensed Im-ages
• Authors: Rui Li, Shunyi Zheng, Chenxi Duan
• Abstract summary: We introduce the Feature Pyramid Net-work (FPN) to bridge the gap between the low-level and high-level features. We propose the Feature Pyramid Network with Multi-Head Attention (FPN-MHA) for semantic segmentation of fine-resolution remotely sensed images.
• Score: 4.869987958751064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation from fine-resolution remotely sensed images is an urgent issue in satellite imagery processing. Due to the complicated environment, automatic categorization and segmen-tation is a challenging matter especially for images with a fine resolution. Solving it can help to surmount a wide varied range of obstacles in urban planning, environmental protection, and natural landscape monitoring, which paves the way for complete scene understanding. However, the existing frequently-used encoder-decoder structure is unable to effectively combine the extracted spatial and contextual features. Therefore, in this paper, we introduce the Feature Pyramid Net-work (FPN) to bridge the gap between the low-level and high-level features. Moreover, we enhance the contextual information with the elaborate Multi-Head Attention module and propose the Feature Pyramid Network with Multi-Head Attention (FPN-MHA) for semantic segmentation of fine-resolution remotely sensed images. Extensive experiments conducted on the ISPRS Potsdam and Vaihingen datasets demonstrate the effectiveness of our FPN-MHA. Code is available at https://github.com/lironui/FPN-MHA.

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