AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation
This addresses aerial image segmentation for remote sensing applications, but it is incremental as it builds on existing Transformer and CNN architectures.
The paper tackled aerial image segmentation by proposing AerialFormer, a hybrid model combining Transformers and lightweight multi-dilated CNNs, which outperformed previous state-of-the-art methods on datasets like iSAID, LoveDA, and Potsdam.
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.