Abstract
Hyperspectral image fusion frequently leverages panchromatic and multispectral images. Although remote sensing images exhibit multi-scale features, prior research has predominantly focused on local feature extraction using convolutional approaches, thereby neglecting long-range dependencies among image elements. To overcome this limitation, we introduce a network for multi-scale long-distance feature extraction that incorporates an encoder-decoder structure with skip connections and a multi-layer perceptron block with attention mechanisms. By capturing features from multiple scales and distant locations within the image, the proposed network improves the performance of image fusion. Our experimental findings demonstrate that the proposed network achieves state-of-the-art performance in image fusion tasks.
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Acknowledgements
I would like to express my deepest gratitude to my thesis advisor Shuyue Luo, for their guidance, support, and patience throughout the research process. Their insightful feedback and expertise have been invaluable in shaping this thesis. I would like to thank my colleagues Yuhao Lian, for their helpful discussions, encouragement, and assistance in various stages of my research. Their insights and ideas have greatly contributed to the development of this thesis. My gratitude also goes to my family and friends for their unwavering support, encouragement, and love. Their emotional support has helped me to stay motivated and focused during the difficult times of my research. Lastly, I would like to thank the participants of my study, without whom this research would not have been possible. Their willingness to participate and share their experiences and insights have been crucial to the success of this study. Thank you all for your invaluable support and contributions.
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Wang, Y., Lian, Y. (2023). MLFEN: Multi-scale Long-Distance Feature Extraction Network. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14134. Springer, Cham. https://doi.org/10.1007/978-3-031-43085-5_15
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DOI: https://doi.org/10.1007/978-3-031-43085-5_15
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