[1] J F DADRASS, F SAMADZADEGAN, S MEHRAVAR et al. A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 171, 101-117(2021).

[2] Z WANG, Y MA, Y ZHANG. Review of pixel-level remote sensing image fusion based on deep learning. Information Fusion, 90, 36-58(2023).

[3] G VIVONE, A GARZELLI. A benchmarking protocol for pansharpening: dataset, preprocessing, and quality assessment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 17(2021).

[4] G VIVONE, L ALPARONE, J CHANUSSOT et al. A Critical comparison among pansharpening algorithms. IEEE Transactions on Geoscience and Remote Sensing, 53, 2565-2586(2015).

[5] B AIAZZI, L ALPARONE, S BARONTI et al. MTF-tailored multiscale fusion of high-resolution MS and pan imagery. Photogrammetric Engineering & Remote Sensing, 72, 591-596(2006).

[6] P SIRGUEY, R MATHIEU, Y ARNAUD et al. Improving MODIS spatial resolution for snow mapping using wavelet fusion and ARSIS Concept. IEEE Geoscience and Remote Sensing Letters, 5, 78-82(2008).

[7] V P SHAH, N H YOUNAN, R L KING. An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets. IEEE Transactions on Geoscience and Remote Sensing, 46, 1323-1335(2008).

[8] M CHOI. A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter. IEEE Transactions on Geoscience and Remote Sensing, 44, 1672-1682(2006).

[9] C BALLESTER, V CASELLES, L IGUAL et al. A variational model for P+XS image fusion. International Journal of Computer Vision, 69, 43-58(2006).

[10] G MASI, D COZZOLINO, L VERDOLIVA et al. Pansharpening by Convolutional Neural Networks. Remote Sensing, 8, 594(2016).

[11] J YANG, X FU, Y HU et al. PanNet: a deep network architecture for pan-sharpening, 10012-10022(2021).

[12] Q YUAN, Y WEI, X MENG et al. A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 978-989(2018).

[13] A DOSOVITSKIY, L BEYER, A KOLESNIKOV et al. An image is worth 16×16 words: Transformers for image recognition at scale. arXiv Preprint(2020).

[14] Z LIU, Y LIN, Y CAO et al. Swin transformer: hierarchical vision transformer using shifted windows, 10012-10022(2021).

[15] H ZHOU, Q LIU, Y WANG. PanFormer: a transformer based model for pan-sharpening, 1-6(2022).

[16] Wensheng FAN, Fan LIU, Ming LI. Remote sensing image fusion based on double-branch u-shaped transformer. Acta Photonica Sinica, 52, 0428002(2023).

[17] Ming LI, Fan LIU, Jingzhi LI. Remote sensing image fusion with detail injection combining convolutional attention module and convolutional autoencoder. Acta Photonica Sinica, 51, 0610005(2022).

[18] B LI, W CUI, W WANG et al. Two-stream convolution augmented transformer for human activity recognition, 35, 286-293(2021).

[19] O RONNEBERGER, P FISCHER, T BROX. U-Net: convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention, 234-241(2015).

[20] X MENG, Y XIONG, F SHAO et al. A large-scale benchmark data set for evaluating pansharpening performance: overview and implementation. IEEE Geoscience and Remote Sensing Magazine, 9, 18-52(2021).

[21] Z XIANG, L XIAO, J YANG et al. Detail-injection-model-inspired deep fusion network for pansharpening. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15(2022).

[22] Z R IN, T J ZHANG, T X JIANG et al. LAGConv: local-context adaptive convolution kernels with global harmonic bias for pansharpening, 36, 1113-1121(2022).

[23] X LIU, Q LIU, Y WANG. Remote sensing image fusion based on two-stream fusion network. Information Fusion, 55, 1-15(2020).