[1] Chave J, Davies S J, Phillips O L et al. Ground data are essential for biomass remote sensing missions[J]. Surveys in Geophysics, 40, 863-880(2019).

[2] Li Z, Wang Y C, Zhang N et al. Deep learning-based object detection techniques for remote sensing images: a survey[J]. Remote Sensing, 14, 2385(2022).

[3] Shafique A, Cao G, Khan Z et al. Deep learning-based change detection in remote sensing images: a review[J]. Remote Sensing, 14, 871(2022).

[4] Li J Y, Huang X, Tu L L et al. A review of building detection from very high resolution optical remote sensing images[J]. GIScience & Remote Sensing, 59, 1199-1225(2022).

[5] Lu Z S, Kan X, Li Y et al. Super-resolution reconstruction of FY-4 images based on matching extraction and cross-scale feature fusion network[J]. Laser & Optoelectronics Progress, 60, 1410013(2023).

[6] Dong C, Loy C C, He K M et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2016).

[7] Ledig C, Theis L, Huszár F et al. Photo-realistic single image super-resolution using a generative adversarial network[C], 105-114(2017).

[8] Wang X T, Yu K, Wu S X et al. ESRGAN: enhanced super-resolution generative adversarial networks[EB/OL]. https://doi.org/10.48550/arXiv.1809.00219

[9] Baghel N, Dubey S, Singh S K. SRTransGAN: image super-resolution using transformer based generative adversarial network[EB/OL]. https://doi.org/10.48550/arXiv.2312.01999

[10] Colin R, Noam S, Adam R et al. Exploring the limits of transfer learning with a unified text-to-text transformer[J]. Journal of Machine Learning Research, 21, 1401-1406(2020).

[11] Brock A, Jeff D, Karen S. Large scale GAN training for high fidelity natural image synthesis[EB/OL]. https://doi.org/10.48550/arXiv.1809.11096

[12] Niu M J, Zhang Y J, Li Z et al. Remote sensing image segmentation network based on adaptive multiscale and contour gradient[J]. Laser & Optoelectronics Progress, 60, 0228009(2023).

[13] Pacal I, Alaftekin M, Zengul F D. Enhancing skin cancer diagnosis using swin transformer with hybrid shifted window-based multi-head self-attention and SwiGLU-based MLP[J]. Journal of Imaging Informatics in Medicine, 37, 3174-3192(2024).

[14] Kai S, Guo J L, Tan X J et al. A study on ReLU and Softmax in transformer[EB/OL]. https://doi.org/10.48550/arXiv.2302.06461

[15] He X Y, Xu W M, Pan K X et al. Classification of high-resolution remote sensing image based on Swin transformer and convolutional neural network[J]. Laser & Optoelectronics Progress, 61, 1428002(2024).

[16] Agarap A F. Deep learning using rectified linear units (ReLU)[EB/OL]. https://doi.org/10.48550/arXiv.1803.08375

[17] Wang Z H, Chen J, Hoi S C H. Deep learning for image super-resolution: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 3365-3387(2021).

[18] Johnson J, Alahi A, Li F F. Perceptual losses for real-time style transfer and super-resolution[EB/OL]. https://doi.org/10.48550/arXiv.1603.08155

[19] Ni Z K, Yang W H, Wang S Q et al. Towards unsupervised deep image enhancement with generative adversarial network[J]. IEEE Transactions on Image Processing, 29, 9140-9151(2020).

[20] Chen M, Pu Y F, Bai Y C. Low-dose CT image denoising using residual convolutional network with fractional TV loss[J]. Neurocomputing, 452, 510-520(2021).

[21] Souza C M, Bastos D S, Filho L A S et al. A study of training approaches of a hybrid summarisation model applied to patent dataset[J]. Journal of Information & Knowledge Management, 22, 2350030(2023).

[22] Xia G S, Hu J W, Hu F et al. AID: a benchmark data set for performance evaluation of aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 3965-3981(2017).

[23] Xia G S, Yang W, Delon J et al. Structural high-resolution satellite image indexing[EB/OL]. https://api.semanticscholar.org/CorpusID:18018842

[24] Cheng G, Han J W, Lu X Q. Remote sensing image scene classification: benchmark and state of the art[J]. Proceedings of the IEEE, 105, 1865-1883(2017).

[25] Yang Y, Newsam S. Bag-of-visual-words and spatial extensions for land-use classification[C], 270-279(2010).

[26] Huynh-Thu Q, Ghanbari M. Scope of validity of PSNR in image/video quality assessment[J]. Electronics Letters, 44, 800-801(2008).

[27] Sho M. Properties of the SSIM metric in medical image assessment: correspondence between measurements and the spatial frequency spectrum[J]. Physical and Engineering Sciences in Medicine, 46, 1131-1141(2023).

[28] Xiang R, Yang H, Yan Z J et al. Super-resolution reconstruction of GOSAT CO2 products using bicubic interpolation[J]. Geocarto International, 37, 15187-15211(2022).

[29] Park J, Son S, Lee K M. Content-aware local GAN for photo-realistic super-resolution[C], 10551-10560(2023).

[30] Sun L, Dong J X, Tang J H et al. Spatially-adaptive feature modulation for efficient image super-resolution[C], 13144-13153(2023).

[31] Zhou Y P, Li Z, Guo C L et al. SRFormerV2: taking a closer look at permuted self-attention for image super-resolution[EB/OL]. https://arxiv.org/html/2303.09735v2