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Meanwhile, GF-6 added four bands, including the \u201cred-edge\u201d band that can effectively reflect the unique spectral characteristics of crops. However, GF-1 data do not contain these bands, which greatly limits their application to crop-related joint monitoring. In this paper, we propose a spectral reconstruction network (SRT) based on Transformer and ResNet to reconstruct the missing bands of GF-1. SRT is composed of three modules: (1) The transformer feature extraction module (TFEM) fully extracts the correlation features between spectra. (2) The residual dense module (RDM) reconstructs local features and avoids the vanishing gradient problem. (3) The residual global construction module (RGM) reconstructs global features and preserves texture details. Compared with competing methods, such as AWAN, HRNet, HSCNN-D, and M2HNet, the proposed method proved to have higher accuracy by a margin of the mean relative absolute error (MRAE) and root mean squared error (RMSE) of 0.022 and 0.009, respectively. It also achieved the best accuracy in supervised classification based on support vector machine (SVM) and spectral angle mapper (SAM).<\/jats:p>","DOI":"10.3390\/rs14133163","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T20:59:18Z","timestamp":1656968358000},"page":"3163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["SRT: A Spectral Reconstruction Network for GF-1 PMS Data Based on Transformer and ResNet"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6624-8389","authenticated-orcid":false,"given":"Kai","family":"Mu","sequence":"first","affiliation":[{"name":"The School of Software, Xinjiang University, Urumqi 830091, China"},{"name":"The Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi 830091, China"},{"name":"The Key Laboratory of Software Engineering, Xinjiang University, Urumqi 830091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"The School of Software, Xinjiang University, Urumqi 830091, China"},{"name":"The Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi 830091, China"},{"name":"The Key Laboratory of Software Engineering, Xinjiang University, Urumqi 830091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yurong","family":"Qian","sequence":"additional","affiliation":[{"name":"The School of Software, Xinjiang University, Urumqi 830091, China"},{"name":"The Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi 830091, China"},{"name":"The Key Laboratory of Software Engineering, Xinjiang University, Urumqi 830091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suhong","family":"Liu","sequence":"additional","affiliation":[{"name":"The Department of Geography, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengting","family":"Sun","sequence":"additional","affiliation":[{"name":"The School of Geography and Remote Sensing Science, Xinjiang University, Urumqi 830049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ranran","family":"Qi","sequence":"additional","affiliation":[{"name":"The School of Software, Xinjiang University, Urumqi 830091, China"},{"name":"The Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi 830091, China"},{"name":"The Key Laboratory of Software Engineering, Xinjiang University, Urumqi 830091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wu, Z., Zhang, J., Deng, F., Zhang, S., Zhang, D., Xun, L., Javed, T., Liu, G., Liu, D., and Ji, M. (2021). Fusion of GF and MODIS Data for Regional-Scale Grassland Community Classification with EVI2 Time-Series and Phenological Features. Remote Sens., 13.","DOI":"10.3390\/rs13050835"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jiang, X., Fang, S., Huang, X., Liu, Y., and Guo, L. (2021). Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands. Remote Sens., 13.","DOI":"10.3390\/rs13040579"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kang, Y., Hu, X., Meng, Q., Zou, Y., Zhang, L., Liu, M., and Zhao, M. (2021). Land Cover and Crop Classification Based on Red Edge Indices Features of GF-6 WFV Time Series Data. Remote Sens., 13.","DOI":"10.3390\/rs13224522"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Arad, B., and Ben-Shahar, O. (2016, January 11\u201314). Sparse recovery of hyperspectral signal from natural RGB images. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46478-7_2"},{"key":"ref_5","unstructured":"Aeschbacher, J., Wu, J., and Timofte, R. (2017, January 22\u201329). In defense of shallow learned spectral reconstruction from RGB images. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1109\/TCI.2018.2855445","article-title":"Spectral Reflectance Recovery From a Single RGB Image","volume":"4","author":"Fu","year":"2018","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1109\/LSP.2017.2776167","article-title":"Locally Linear Embedded Sparse Coding for Spectral Reconstruction From RGB Images","volume":"25","author":"Li","year":"2018","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Geng, Y., Mei, S., Tian, J., Zhang, Y., and Du, Q. (August, January 28). Spatial Constrained Hyperspectral Reconstruction from RGB Inputs Using Dictionary Representation. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898871"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2269","DOI":"10.1109\/TGRS.2020.3000684","article-title":"Spectral superresolution of multispectral imagery with joint sparse and low-rank learning","volume":"59","author":"Gao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xiong, Z., Shi, Z., Li, H., Wang, L., Liu, D., and Wu, F. (2017, January 22\u201329). Hscnn: Cnn-based hyperspectral image recovery from spectrally undersampled projections. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.68"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Alvarez-Gila, A., Van De Weijer, J., and Garrote, E. (2017, January 22\u201329). Adversarial networks for spatial context-aware spectral image reconstruction from rgb. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.64"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Koundinya, S., Sharma, H., Sharma, M., Upadhyay, A., Manekar, R., Mukhopadhyay, R., Karmakar, A., and Chaudhury, S. (2018, January 18\u201323). 2D-3D CNN based architectures for spectral reconstruction from RGB images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00129"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shi, Z., Chen, C., Xiong, Z., Liu, D., and Wu, F. (2018, January 18\u201323). Hscnn+: Advanced cnn-based hyperspectral recovery from rgb images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00139"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Po, L.M., Yan, Q., Liu, W., and Lin, T. (2020, January 14\u201319). Hierarchical regression network for spectral reconstruction from RGB images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00219"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.isprsjprs.2021.01.019","article-title":"M2H-Net: A Reconstruction Method For Hyperspectral Remotely Sensed Imagery","volume":"173","author":"Deng","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","first-page":"1","article-title":"Progressive Spatial\u2013Spectral Joint Network for Hyperspectral Image Reconstruction","volume":"60","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1364\/JOSAA.25.000371","article-title":"Reconstructing spectral reflectance by dividing spectral space and extending the principal components in principal component analysis","volume":"25","author":"Zhang","year":"2008","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1109\/LGRS.2015.2417857","article-title":"Improving chlorophyll fluorescence retrieval using reflectance reconstruction based on principal components analysis","volume":"12","author":"Liu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6621","DOI":"10.1364\/AO.39.006621","article-title":"System design for accurately estimating the spectral reflectance of art paintings","volume":"39","author":"Haneishi","year":"2000","journal-title":"Appl. Opt."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Imai, F.H., and Berns, R.S. (1999, January 21\u201322). Spectral estimation using trichromatic digital cameras. Proceedings of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, Chiba, Japan.","DOI":"10.2352\/CIC.1999.7.1.art00005"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1364\/JOSAA.22.001231","article-title":"Characterization of trichromatic color cameras by using a new multispectral imaging technique","volume":"22","author":"Cheung","year":"2005","journal-title":"JOSA A"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, J., Su, R., Ren, W., Fu, Q., and Nie, Y. (2021). Learnable Reconstruction Methods from RGB Images to Hyperspectral Imaging: A Survey. arXiv.","DOI":"10.1038\/s41598-022-16223-1"},{"key":"ref_23","unstructured":"Arad, B., Ben-Shahar, O., Timofte, R., Gool, L.V., and Yang, M.H. (2018, January 18\u201323). NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, J., Wu, C., Song, R., Li, Y., and Liu, F. (2020, January 14\u201319). Adaptive weighted attention network with camera spectral sensitivity prior for spectral reconstruction from RGB images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00239"},{"key":"ref_25","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA."},{"key":"ref_26","unstructured":"Lin, T., Wang, Y., Liu, X., and Qiu, X. (2021). A survey of transformers. arXiv."},{"key":"ref_27","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_28","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and J\u00e9gou, H. (2021, January 13\u201314). Training data-efficient image transformers & distillation through attention. Proceedings of the International Conference on Machine Learning (PMLR), Virtual Event."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 14\u201319). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision, Seattle, WA, USA.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_30","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., and Torr, P.H. (2021, January 20\u201325). Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"ref_32","unstructured":"Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., and Patel, V.M. (October, January 27). Medical transformer: Gated axial-attention for medical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France."},{"key":"ref_33","unstructured":"Arad Hudson, D., and Zitnick, L. (2020, January 6\u201312). Compositional Transformers for Scene Generation. Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Online."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., and Girshick, R. (2021). Masked autoencoders are scalable vision learners. arXiv.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 22\u201329). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Venice, Italy.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_37","unstructured":"LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., and Jackel, L. (1989, January 27\u201330). Handwritten digit recognition with a back-propagation network. Proceedings of the Advances in Neural Information Processing Systems 2, Denver, CO, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1049\/el:20080522","article-title":"Scope of validity of PSNR in image\/video quality assessment","volume":"44","author":"Ghanbari","year":"2008","journal-title":"Electron. Lett."},{"key":"ref_40","unstructured":"(2022, June 02). CRESDA. China Centre For Resources Satellite Data and Application. 2021. Available online: http:\/\/www.cresda.com\/CN\/index.shtml."},{"key":"ref_41","unstructured":"Yuhas, R.H., Goetz, A.F., and Boardman, J.W. (1992). Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm. Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop, Jet Propulsion Laboratory."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3163\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:41:49Z","timestamp":1760139709000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,1]]},"references-count":42,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14133163"],"URL":"https:\/\/doi.org\/10.3390\/rs14133163","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,1]]}}}