MsIFT: Multi-Source Image Fusion Transformer

Multi-source image fusion is very important for improving image representation ability since its essence relies on the complementarity between multi-source information. However, feature-level image fusion methods based on the convolution neural network are impacted by the spatial misalignment betwee...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-08, Vol.14 (16), p.4062
Main Authors: Zhang, Xin, Jiang, Hangzhi, Xu, Nuo, Ni, Lei, Huo, Chunlei, Pan, Chunhong
Format: Article
Language:English
Subjects:
Artificial neural networks
Bias
Classification
Complementarity
Computer vision
Datasets
Image classification
Image processing
Image segmentation
Misalignment
multi-source image fusion
Neural networks
non-local
Pixels
Remote sensing
Representations
Semantic segmentation
Semantics
Support vector machines
transformer
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Summary:Multi-source image fusion is very important for improving image representation ability since its essence relies on the complementarity between multi-source information. However, feature-level image fusion methods based on the convolution neural network are impacted by the spatial misalignment between image pairs, which leads to the semantic bias in merging features and destroys the representation ability of the region-of-interests. In this paper, a novel multi-source image fusion transformer (MsIFT) is proposed. Due to the inherent global attention mechanism of the transformer, the MsIFT has non-local fusion receptive fields, and it is more robust to spatial misalignment. Furthermore, multiple classification-based downstream tasks (e.g., pixel-wise classification, image-wise classification and semantic segmentation) are unified in the proposed MsIFT framework, and the fusion module architecture is shared by different tasks. The MsIFT achieved state-of-the-art performances on the image-wise classification dataset VAIS, semantic segmentation dataset SpaceNet 6 and pixel-wise classification dataset GRSS-DFC-2013. The code and trained model are being released upon the publication of the work.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14164062