AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation

• URL: http://arxiv.org/abs/2306.06842v2
• Date: Sun, 1 Oct 2023 17:04:35 GMT
• Title: AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation
• Authors: Kashu Yamazaki, Taisei Hanyu, Minh Tran, Adrian de Luis, Roy McCann, Haitao Liao, Chase Rainwater, Meredith Adkins, Jackson Cothren, Ngan Le
• Abstract summary: We propose AerialFormer, which unifies Transformers at the contracting path with lightweight Multi-Dilated Conal Neural Networks (MD-CNNs) at the expanding path. Our AerialFormer is designed as a hierarchical structure, in which Transformer outputs multi-scale features and MD-CNNs decoder aggregates information from the multi-scales. We have benchmarked AerialFormer on three common datasets including iSAID, LoveDA, and Potsdam.
• Score: 7.415370401064414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aerial Image Segmentation is a top-down perspective semantic segmentation and has several challenging characteristics such as strong imbalance in the foreground-background distribution, complex background, intra-class heterogeneity, inter-class homogeneity, and tiny objects. To handle these problems, we inherit the advantages of Transformers and propose AerialFormer, which unifies Transformers at the contracting path with lightweight Multi-Dilated Convolutional Neural Networks (MD-CNNs) at the expanding path. Our AerialFormer is designed as a hierarchical structure, in which Transformer encoder outputs multi-scale features and MD-CNNs decoder aggregates information from the multi-scales. Thus, it takes both local and global contexts into consideration to render powerful representations and high-resolution segmentation. We have benchmarked AerialFormer on three common datasets including iSAID, LoveDA, and Potsdam. Comprehensive experiments and extensive ablation studies show that our proposed AerialFormer outperforms previous state-of-the-art methods with remarkable performance. Our source code will be publicly available upon acceptance.

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