{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T22:47:19Z","timestamp":1776898039489,"version":"3.51.2"},"reference-count":71,"publisher":"Association for Computing Machinery (ACM)","issue":"11","funder":[{"name":"Key Research and Development Programme of Heilongjiang","award":["2023ZX01A23"],"award-info":[{"award-number":["2023ZX01A23"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["2572023CT16"],"award-info":[{"award-number":["2572023CT16"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Central Government Guided Local Science and Technology Development","award":["2024ZY0616"],"award-info":[{"award-number":["2024ZY0616"]}]},{"name":"Inner Mongolia Autonomous Region\u2019s unveiling and leadership project","award":["2024JBGS0014"],"award-info":[{"award-number":["2024JBGS0014"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>\n                    Hyperspectral imaging is a valuable technique for accurately classifying materials because of the abundance of spectral information and high resolution it provides. However, the characteristics of Hyperspectral Imaging, such as high-dimensional features and information redundancy, pose significant challenges to data processing. Traditional dimensionality reduction methods often have information loss, high computational complexity, and easy to ignore the strong correlation between HSI bands when dealing with the HSI data. Although other methods can achieve satisfactory classification performance, they do not consider the dimensionality reduction of HSI, and they focus on the model performance, which limits further improvement in classification performance. This article proposes a transformer-based framework called \u201cSpectrumRecombineFormer\u201d (SRF), which is composed of two key modules, namely \u201cSpatial\u2013Spectral Recombination\u201d (SSRC) and \u201cCross-Layer Fusion\u201d (CF). The SSRC is capable of utilizing both adjacent and non-adjacent spectrums to generate the spatial-sequential perceptive representations, which alleviate the effect of the strong correlation between HSI bands. The CF can avoid the loss of information during the feed-forward procedure among layers. Extensive experiments on five existing datasets (widely adopted Indian Pines, Houston2013, Pavia University, Salinas, and KSC) demonstrate the capability of our proposed method to address the above-mentioned challenges. Both quantitative and qualitative experimental ablation studies, including visualization results, reveal that the proposed SRF method can successfully and efficiently classify HSIs and surpass the other state-of-the-art methods. For access to the source code, please visit\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/kangpeilun\/SRF-HSI-Classification-master\">https:\/\/github.com\/kangpeilun\/SRF-HSI-Classification-master<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3715698","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T11:33:36Z","timestamp":1738150416000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["SRF: SpectrumRecombineFormer for Hyperspectral Image Classification"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7933-6946","authenticated-orcid":false,"given":"Weipeng","family":"Jing","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7687-6864","authenticated-orcid":false,"given":"Peilun","family":"Kang","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2270-3378","authenticated-orcid":false,"given":"Donglin","family":"Di","sequence":"additional","affiliation":[{"name":"LiAuto Inc, Shunyi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5701-7204","authenticated-orcid":false,"given":"Juntao","family":"Gu","sequence":"additional","affiliation":[{"name":"Heilongjiang Cyberspace Research Center, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9754-5207","authenticated-orcid":false,"given":"Linhui","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1290-4272","authenticated-orcid":false,"given":"Mahmoud","family":"Emam","sequence":"additional","affiliation":[{"name":"Faculty of Artificial Intelligence, Menoufia University, Menofia, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9308-3578","authenticated-orcid":false,"given":"Linda","family":"Mohaisen","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0201-1638","authenticated-orcid":false,"given":"Xun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1932-7698","authenticated-orcid":false,"given":"Chao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, Harbin, China"}]}],"member":"320","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs9111110"},{"key":"e_1_3_3_3_2","volume-title":"Deep Learning for Hyperspectral Image Classification","author":"Ahmad Muhammad","year":"2021","unstructured":"Muhammad Ahmad. 2021. Deep Learning for Hyperspectral Image Classification. Dissertation. Universit\u00e0 Degli Studi Di Messina."},{"key":"e_1_3_3_4_2","unstructured":"Muhammad Ahmad Muhammad Hassaan Farooq Butt Manuel Mazzara and Salvatore Distifano. 2024. Pyramid hierarchical transformer for hyperspectral image classification. arXiv:2404.14945. Retrieved from https:\/\/arxiv.org\/abs\/2404.14945"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2004.842292"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/IV48863.2021.9575298"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2016.01.011"},{"key":"e_1_3_3_8_2","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/978-3-540-72523-7_50","volume-title":"Proceedings of the International Workshop on Multiple Classifier Systems","author":"Benediktsson Jon Atli","year":"2007","unstructured":"Jon Atli Benediktsson, Jocelyn Chanussot, and Mathieu Fauvel. 2007. Multiple classifier systems in remote sensing: From basics to recent developments. In Proceedings of the International Workshop on Multiple Classifier Systems. Springer, 501\u2013512."},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2584107"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2015.2388577"},{"issue":"1","key":"e_1_3_3_11_2","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1109\/TIP.2018.2867198","article-title":"Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection","volume":"28","author":"Cheng Gong","year":"2018","unstructured":"Gong Cheng, Junwei Han, Peicheng Zhou, and Dong Xu. 2018. Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection. IEEE Transactions on Image Processing 28, 1 (2018), 265\u2013278.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"2","key":"e_1_3_3_12_2","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2017.2786223","article-title":"Modified tensor locality preserving projection for dimensionality reduction of hyperspectral images","volume":"15","author":"Deng Yang-Jun","year":"2018","unstructured":"Yang-Jun Deng, Heng-Chao Li, Lei Pan, Li-Yang Shao, Qian Du, and William J Emery. 2018. Modified tensor locality preserving projection for dimensionality reduction of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 15, 2 (2018), 277\u2013281.","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"e_1_3_3_13_2","unstructured":"Jacob Devlin. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805. Retrieved from https:\/\/arxiv.org\/abs\/1810.04805"},{"key":"e_1_3_3_14_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3328922","article-title":"UNFOLD: 3D U-Net, 3D CNN and 3D transformer based hyperspectral image denoising","volume":"61","author":"Dixit Aditya","year":"2023","unstructured":"Aditya Dixit, Anup Kumar Gupta, Puneet Gupta, Saurabh Srivastava, and Ankur Garg. 2023. UNFOLD: 3D U-Net, 3D CNN and 3D transformer based hyperspectral image denoising. IEEE Transactions on Geoscience and Remote Sensing 61 (2023), 1\u201310.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2645703"},{"key":"e_1_3_3_16_2","unstructured":"Alexey Dosovitskiy. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929. Retrieved from https:\/\/arxiv.org\/abs\/2010.11929"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2007.900751"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2021.3129818"},{"issue":"10","key":"e_1_3_3_19_2","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1109\/LGRS.2014.2306689","article-title":"Dimensionality reduction of hyperspectral images based on robust spatial information using locally linear embedding","volume":"11","author":"Fang Yu","year":"2014","unstructured":"Yu Fang, Hao Li, Yong Ma, Kun Liang, Yingjie Hu, Shaojie Zhang, and Hongyuan Wang. 2014. Dimensionality reduction of hyperspectral images based on robust spatial information using locally linear embedding. IEEE Geoscience and Remote Sensing Letters 11, 10 (2014), 1712\u20131716.","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107070"},{"key":"e_1_3_3_21_2","first-page":"1","article-title":"Hyperspectral image denoising via spatial-spectral recurrent transformer","volume":"62","author":"Fu Guanyiman","year":"2024","unstructured":"Guanyiman Fu, Fengchao Xiong, Jianfeng Lu, Jun Zhou, Jiantao Zhou, and Yuntao Qian. 2024. Hyperspectral image denoising via spatial-spectral recurrent transformer. IEEE Transactions on Geoscience and Remote Sensing 62 (2024), 1\u201313.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2766242"},{"issue":"9","key":"e_1_3_3_23_2","doi-asserted-by":"crossref","first-page":"349","DOI":"10.3390\/ijgi7090349","article-title":"Joint alternate small convolution and feature reuse for hyperspectral image classification","volume":"7","author":"Gao Hongmin","year":"2018","unstructured":"Hongmin Gao, Yao Yang, Chenming Li, Hui Zhou, and Xiaoyu Qu. 2018. Joint alternate small convolution and feature reuse for hyperspectral image classification. ISPRS International Journal of Geo-Information 7, 9 (2018), 349.","journal-title":"ISPRS International Journal of Geo-Information"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2006.878240"},{"issue":"9","key":"e_1_3_3_25_2","doi-asserted-by":"crossref","first-page":"3262","DOI":"10.1109\/JSTARS.2018.2847042","article-title":"Dimensionality reduction of hyperspectral image using spatial regularized local graph discriminant embedding","volume":"11","author":"Hang Renlong","year":"2018","unstructured":"Renlong Hang and Qingshan Liu. 2018. Dimensionality reduction of hyperspectral image using spatial regularized local graph discriminant embedding. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, 9 (2018), 3262\u20133271.","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"8","key":"e_1_3_3_26_2","doi-asserted-by":"crossref","first-page":"), 5384","DOI":"10.1109\/TGRS.2019.2899129","article-title":"Cascaded recurrent neural networks for hyperspectral image classification","volume":"57","author":"Hang Renlong","year":"2019","unstructured":"Renlong Hang, Qingshan Liu, Danfeng Hong, and Pedram Ghamisi. 2019. Cascaded recurrent neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 57, 8 (2019), 5384\u20135394.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"1","key":"e_1_3_3_27_2","first-page":"165","article-title":"HSI-BERT: Hyperspectral image classification using the bidirectional encoder representation from transformers","volume":"58","author":"He Ji","year":"2019","unstructured":"Ji He, Lina Zhao, Hongwei Yang, Mengmeng Zhang, and Wei Li. 2019. HSI-BERT: Hyperspectral image classification using the bidirectional encoder representation from transformers. IEEE Transactions on Geoscience and Remote Sensing 58, 1 (2019), 165\u2013178.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_3_28_2","first-page":"770","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"He Kaiming","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. [n.\u2009d.]. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770\u2013778."},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/5254.708428"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3172371"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2013.2259470"},{"issue":"6","key":"e_1_3_3_32_2","doi-asserted-by":"crossref","first-page":"2604","DOI":"10.1109\/TCYB.2019.2905793","article-title":"Dimensionality reduction of hyperspectral imagery based on spatial\u2013spectral manifold learning","volume":"50","author":"Huang Hong","year":"2019","unstructured":"Hong Huang, Guangyao Shi, Haibo He, Yule Duan, and Fulin Luo. 2019. Dimensionality reduction of hyperspectral imagery based on spatial\u2013spectral manifold learning. IEEE Transactions on Cybernetics 50, 6 (2019), 2604\u20132616.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2008.2008308"},{"issue":"4","key":"e_1_3_3_34_2","first-page":"2337","article-title":"Rotation-insensitive and context-augmented object detection in remote sensing images","volume":"56","author":"Li Ke","year":"2017","unstructured":"Ke Li, Gong Cheng, Shuhui Bu, and Xiong You. 2017. Rotation-insensitive and context-augmented object detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 56, 4 (2017), 2337\u20132348.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3263109"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3366536"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00339-011-6689-1"},{"issue":"3","key":"e_1_3_3_38_2","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/LGRS.2011.2172185","article-title":"Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles","volume":"9","author":"Licciardi Giorgio","year":"2011","unstructured":"Giorgio Licciardi, Prashanth Reddy Marpu, Jocelyn Chanussot, and Jon Atli Benediktsson. 2011. Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles. IEEE Geoscience and Remote Sensing Letters 9, 3 (2011), 447\u2013451.","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"10","key":"e_1_3_3_39_2","first-page":"8657","article-title":"CNN-enhanced graph convolutional network with pixel-and superpixel-level feature fusion for hyperspectral image classification","volume":"59","author":"Liu Qichao","year":"2020","unstructured":"Qichao Liu, Liang Xiao, Jingxiang Yang, and Zhihui Wei. 2020. CNN-enhanced graph convolutional network with pixel-and superpixel-level feature fusion for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 59, 10 (2020), 8657\u20138671.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"11","key":"e_1_3_3_41_2","first-page":"4099","article-title":"Local manifold learning-based k-nearest-neighbor for hyperspectral image classification","volume":"48","author":"Ma Li","year":"2010","unstructured":"Li Ma, Melba M. Crawford, and Jinwen Tian. 2010. Local manifold learning-based k-nearest-neighbor for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 48, 11 (2010), 4099\u20134109.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_3_42_2","doi-asserted-by":"crossref","first-page":"4959","DOI":"10.1109\/IGARSS.2015.7326945","volume-title":"Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","author":"Makantasis Konstantinos","year":"2015","unstructured":"Konstantinos Makantasis, Konstantinos Karantzalos, Anastasios Doulamis, and Nikolaos Doulamis. 2015. Deep supervised learning for hyperspectral data classification through convolutional neural networks. In Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 4959\u20134962."},{"issue":"8","key":"e_1_3_3_43_2","doi-asserted-by":"crossref","first-page":"e13311","DOI":"10.1111\/exsy.13311","article-title":"Hyperspectral imaging for early diagnosis of diseases: A review","volume":"40","author":"Mangotra Harshita","year":"2023","unstructured":"Harshita Mangotra, Sahima Srivastava, Garima Jaiswal, Ritu Rani, and Arun Sharma. 2023. Hyperspectral imaging for early diagnosis of diseases: A review. Expert Systems 40, 8 (2023), e13311.","journal-title":"Expert Systems"},{"key":"e_1_3_3_44_2","first-page":"1","article-title":"Hyperspectral image classification using group-aware hierarchical transformer","volume":"60","author":"Mei Shaohui","year":"2022","unstructured":"Shaohui Mei, Chao Song, Mingyang Ma, and Fulin Xu. 2022. Hyperspectral image classification using group-aware hierarchical transformer. IEEE Transactions on Geoscience and Remote Sensing 60 (2022), 1\u201314.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2636241"},{"key":"e_1_3_3_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.09.006"},{"key":"e_1_3_3_47_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4939-2836-1","volume-title":"Hyperspectral Imaging Technology in Food and Agriculture","author":"Park Bosoon","year":"2015","unstructured":"Bosoon Park and Renfu Lu. 2015. Hyperspectral Imaging Technology in Food and Agriculture. Vol. 1. Springer."},{"key":"e_1_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2020.2979764"},{"key":"e_1_3_3_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"issue":"4","key":"e_1_3_3_50_2","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1080\/14498596.2022.2074902","article-title":"Spectral segmentation based dimension reduction for hyperspectral image classification","volume":"68","author":"Siddiqa Ayasha","year":"2023","unstructured":"Ayasha Siddiqa, Rashedul Islam, and Masud Ibn Afjal. 2023. Spectral segmentation based dimension reduction for hyperspectral image classification. Journal of Spatial Science 68, 4 (2023), 543\u2013562.","journal-title":"Journal of Spatial Science"},{"key":"e_1_3_3_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3231215"},{"issue":"6","key":"e_1_3_3_52_2","doi-asserted-by":"crossref","first-page":"), 3906","DOI":"10.1109\/TGRS.2019.2959342","article-title":"Fast and latent low-rank subspace clustering for hyperspectral band selection","volume":"58","author":"Sun Weiwei","year":"2020","unstructured":"Weiwei Sun, Jiangtao Peng, Gang Yang, and Qian Du. 2020. Fast and latent low-rank subspace clustering for hyperspectral band selection. IEEE Transactions on Geoscience and Remote Sensing 58, 6 (2020), 3906\u20133915.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"7","key":"e_1_3_3_53_2","doi-asserted-by":"crossref","first-page":"4032","DOI":"10.1109\/TGRS.2017.2686842","article-title":"A sparse and low-rank near-isometric linear embedding method for feature extraction in hyperspectral imagery classification","volume":"55","author":"Sun Weiwei","year":"2017","unstructured":"Weiwei Sun, Gang Yang, Bo Du, Lefei Zhang, and Liangpei Zhang. 2017. A sparse and low-rank near-isometric linear embedding method for feature extraction in hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing 55, 7 (2017), 4032\u20134046.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_3_54_2","doi-asserted-by":"publisher","DOI":"10.1080\/01431160600887706"},{"key":"e_1_3_3_55_2","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani A","year":"2017","unstructured":"A Vaswani. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017), 5998\u20136008.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_56_2","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017), 1\u201311.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2011.2153861"},{"key":"e_1_3_3_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3173474"},{"key":"e_1_3_3_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2010.2041784"},{"issue":"6","key":"e_1_3_3_60_2","doi-asserted-by":"crossref","first-page":"1652","DOI":"10.3390\/s20061652","article-title":"Three-dimensional ResNeXt network using feature fusion and label smoothing for hyperspectral image classification","volume":"20","author":"Wu Peida","year":"2020","unstructured":"Peida Wu, Ziguan Cui, Zongliang Gan, and Feng Liu. 2020. Three-dimensional ResNeXt network using feature fusion and label smoothing for hyperspectral image classification. Sensors 20, 6 (2020), 1652.","journal-title":"Sensors"},{"key":"e_1_3_3_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2965302"},{"key":"e_1_3_3_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2698503"},{"key":"e_1_3_3_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2010.2043533"},{"key":"e_1_3_3_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3171551"},{"issue":"7","key":"e_1_3_3_65_2","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1109\/LGRS.2019.2891076","article-title":"Hyperspectral image classification using CapsNet with well-initialized shallow layers","volume":"16","author":"Yin Jihao","year":"2019","unstructured":"Jihao Yin, Sen Li, Hongmei Zhu, and Xiaoyan Luo. 2019. Hyperspectral image classification using CapsNet with well-initialized shallow layers. IEEE Geoscience and Remote Sensing Letters 16, 7 (2019), 1095\u20131099.","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"e_1_3_3_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2540798"},{"key":"e_1_3_3_67_2","first-page":"1","article-title":"Spectral\u2013spatial and superpixelwise PCA for unsupervised feature extraction of hyperspectral imagery","volume":"60","author":"Zhang Xin","year":"2021","unstructured":"Xin Zhang, Xinwei Jiang, Junjun Jiang, Yongshan Zhang, Xiaobo Liu, and Zhihua Cai. 2021. Spectral\u2013spatial and superpixelwise PCA for unsupervised feature extraction of hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 60 (2021), 1\u201310.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"8","key":"e_1_3_3_68_2","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1109\/LGRS.2019.2945546","article-title":"Semisupervised classification based on SLIC segmentation for hyperspectral image","volume":"17","author":"Zhang Yuxiang","year":"2019","unstructured":"Yuxiang Zhang, Kang Liu, Yanni Dong, Ke Wu, and Xiangyun Hu. 2019. Semisupervised classification based on SLIC segmentation for hyperspectral image. IEEE Geoscience and Remote Sensing Letters 17, 8 (2019), 1440\u20131444.","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"e_1_3_3_69_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3116147","article-title":"Generalized scene classification from small-scale datasets with multitask learning","volume":"60","author":"Zheng Xiangtao","year":"2021","unstructured":"Xiangtao Zheng, Tengfei Gong, Xiaobin Li, and Xiaoqiang Lu. 2021. Generalized scene classification from small-scale datasets with multitask learning. IEEE Transactions on Geoscience and Remote Sensing 60 (2021), 1\u201311.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"9","key":"e_1_3_3_70_2","doi-asserted-by":"crossref","first-page":"5185","DOI":"10.1109\/TGRS.2017.2703598","article-title":"Dimensionality reduction by spatial\u2013spectral preservation in selected bands","volume":"55","author":"Zheng Xiangtao","year":"2017","unstructured":"Xiangtao Zheng, Yuan Yuan, and Xiaoqiang Lu. 2017. Dimensionality reduction by spatial\u2013spectral preservation in selected bands. IEEE Transactions on Geoscience and Remote Sensing 55, 9 (2017), 5185\u20135197.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_3_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2893115"},{"key":"e_1_3_3_72_2","first-page":"1","article-title":"Spectral\u2013spatial transformer network for hyperspectral image classification: A factorized architecture search framework","volume":"60","author":"Zhong Zilong","year":"2021","unstructured":"Zilong Zhong, Ying Li, Lingfei Ma, Jonathan Li, and Wei-Shi Zheng. 2021. Spectral\u2013spatial transformer network for hyperspectral image classification: A factorized architecture search framework. IEEE Transactions on Geoscience and Remote Sensing 60 (2021), 1\u201315.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3715698","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T15:09:26Z","timestamp":1762528166000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3715698"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":71,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11,30]]}},"alternative-id":["10.1145\/3715698"],"URL":"https:\/\/doi.org\/10.1145\/3715698","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]},"assertion":[{"value":"2024-06-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}