{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:36:03Z","timestamp":1763202963411,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62077038","61672405","62176196","62271374"],"award-info":[{"award-number":["62077038","61672405","62176196","62271374"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral images have the characteristics of high spectral resolution and low spatial resolution, which will make the extracted features insufficient and lack detailed information about ground objects, thus affecting the accuracy of classification. The numerous spectral bands of hyperspectral images contain rich spectral features but also bring issues of noise and redundancy. To improve the spatial resolution and fully extract spatial and spectral features, this article proposes an improved feature enhancement and extraction model (IFEE) using spatial feature enhancement and attention-guided bidirectional sequential spectral feature extraction for hyperspectral image classification. The adaptive guided filtering is introduced to highlight details and edge features in hyperspectral images. Then, an image enhancement module composed of two-dimensional convolutional neural networks is used to improve the resolution of the image after adaptive guidance filtering and provide a high-resolution image with key features emphasized for the subsequent feature extraction module. The proposed spectral attention mechanism helps to extract more representative spectral features, emphasizing useful information while suppressing the interference of noise. Experimental results show that our method outperforms other comparative methods even with very few training samples.<\/jats:p>","DOI":"10.3390\/rs16173124","type":"journal-article","created":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T03:14:31Z","timestamp":1724642071000},"page":"3124","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Spatial Feature Enhancement and Attention-Guided Bidirectional Sequential Spectral Feature Extraction for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9993-0731","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Shanjiao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China"}]},{"given":"Yijin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4373-3661","authenticated-orcid":false,"given":"Caihong","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sun, G., Zhang, A., Ren, J., Ma, J., Wang, P., Zhang, Y., and Jia, X. (2017). Gravitation-based edge detection in hyperspectral images. Remote Sens., 9.","DOI":"10.20944\/preprints201705.0142.v1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1109\/TGRS.2016.2646420","article-title":"Estimating soil salinity under various moisture conditions: An experimental study","volume":"55","author":"Yang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1037\/h0071325","article-title":"Analysis of a complex of statistical variables into principal components","volume":"24","author":"Hotelling","year":"1933","journal-title":"J. Educ. Psychol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TGRS.2005.846154","article-title":"Kernel-based methods for hyperspectral image classification","volume":"43","author":"Bruzzone","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1016\/j.patcog.2010.01.016","article-title":"Segmentation and classification of hyperspectral images using watershed transformation","volume":"43","author":"Tarabalka","year":"2010","journal-title":"Pattern Recogn."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided image filtering","volume":"35","author":"He","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.1109\/TIP.2013.2244222","article-title":"Image fusion with guided filtering","volume":"22","author":"Li","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","first-page":"345","article-title":"High efficient deep feature extraction and classification of spectral-spatial hyperspectral image using cross domain convolutional neural networks","volume":"12","author":"Guo","year":"2019","journal-title":"IEEE J. Sel. Topics in Appl. Earth Observ. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral-spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep feature extraction and classification of hyperspectral images based on convolutional neural networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","article-title":"Deep recurrent neural networks for hyperspectral image classification","volume":"55","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-excitation networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J., and Kweon, I. (2018, January 8\u201314). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral-spatial residual network for hyperspectral image classification: A 3-D deep learning framework","volume":"56","author":"Zhong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.1109\/TGRS.2018.2794326","article-title":"Hyperspectral image classification with deep feature fusion network","volume":"56","author":"Song","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mu, C., Guo, Z., and Liu, Y. (2020). A multi-scale and multi-level spectral-spatial feature fusion network for hyperspectral image classification. Remote Sens., 12.","DOI":"10.3390\/rs12010125"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","article-title":"HybridSN: Exploring 3-D\u20132-D CNN feature hierarchy for hyperspectral image classification","volume":"17","author":"Swalpa","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"0196","DOI":"10.1109\/TGRS.2022.3221534","article-title":"Spectral\u2013spatial feature tokenization transformer for hyperspectral image classification","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mu, C., Liu, Y., and Liu, Y. (2021). Hyperspectral image spectral-spatial classification method based on deep adaptive feature fusion. Remote Sens., 13.","DOI":"10.3390\/rs13040746"},{"key":"ref_22","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TGRS.2019.2934760","article-title":"HSI-BERT: Hyperspectral image classification using the bidirectional encoder representation from transformers","volume":"58","author":"He","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3172371","article-title":"Spectralformer: Rethinking hyperspectral image classification with transformers","volume":"60","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","first-page":"1","article-title":"Convolution transformer mixer for hyperspectral image classification","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","first-page":"1","article-title":"Hyperspectral image transformer classification networks","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","first-page":"1","article-title":"Interactive spectral-spatial transformer for hyperspectral image classification","volume":"1","author":"Song","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patrec.2023.12.023","article-title":"SemanticFormer: Hyperspectral image classification via semantic transformer","volume":"179","author":"Liu","year":"2024","journal-title":"Pattern Recognit. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"110006","DOI":"10.1016\/j.patcog.2023.110006","article-title":"Weighted side-window based gradient guided image filtering","volume":"146","author":"Yuan","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tyagi, V. (2018). Image enhancement in spatial domain. Understanding Digital Image Processing, CRC Press.","DOI":"10.1201\/9781315123905"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, X., Qin, H., Yu, Y., Yan, X., Yang, S., and Wang, G. (2023). Unsupervised low-light image enhancement via virtual diffraction information in frequency domain. Remote Sens., 15.","DOI":"10.20944\/preprints202306.0787.v1"},{"key":"ref_33","first-page":"1","article-title":"Spatial-frequency dual-domain feature fusion network for low-light remote sensing image enhancement","volume":"1","author":"Yao","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, Z., Yang, J., and Zhang, H. (2023). Wavelet transform feature enhancement for semantic segmentation of remote sensing images. Remote Sens., 15.","DOI":"10.3390\/rs15245644"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"035019","DOI":"10.1063\/5.0141666","article-title":"Toward understanding the effectiveness of attention mechanism","volume":"13","author":"Ye","year":"2023","journal-title":"AIP Adv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Feng, Y., Zhu, X., Zhang, X., Li, Y., and Lu, H. (2024). PAMSNet: A medical image segmentation network based on spatial pyramid and attention mechanism. Biomed. Signal Proces., 94.","DOI":"10.1016\/j.bspc.2024.106285"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"108261","DOI":"10.1016\/j.engappai.2024.108261","article-title":"Multi-scale spatial pyramid attention mechanism for image recognition: An effective approach","volume":"133","author":"Yu","year":"2024","journal-title":"Eng. Appl. Artif. Intel."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kang, J., Zhang, Y., Liu, X., and Cheng, Z. (2024). Hyperspectral image classification using spectral-spatial double-branch attention mechanism. Remote Sens., 16.","DOI":"10.3390\/rs16010193"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5427","DOI":"10.1007\/s40747-024-01445-9","article-title":"An attention mechanism module with spatial perception and channel information interaction","volume":"10","author":"Wang","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"An, W., and Wu, G. (2024). Hybrid spatial-channel attention mechanism for cross-age face recognition. Electronics, 13.","DOI":"10.3390\/electronics13071257"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1109\/TCSVT.2022.3218284","article-title":"Exploring the relationship between center and neighborhoods: Central vector oriented self-similarity network for hyperspectral image classification","volume":"33","author":"Li","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, L., Ruan, C., Zhao, J., and Huang, L. (2024, January 19\u201321). Triple-attention residual networks for hyperspectral image classification. Proceedings of the International Conference on Computer Vision, Image and Deep Learning (CVIDL), Zhuhai, China.","DOI":"10.1109\/CVIDL62147.2024.10604125"},{"key":"ref_43","unstructured":"Meng, Z., Yan, Q., Zhao, F., and Liang, M. (November, January 31). Hyperspectral image classification with dynamic spatial-spectral attention network. Proceedings of the Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Athens, Greece."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_45","first-page":"1","article-title":"Vision transformer with contrastive learning for hyperspectral image classification","volume":"20","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5548","DOI":"10.1080\/01431161.2023.2249598","article-title":"CNN and Transformer interaction network for hyperspectral image classification","volume":"44","author":"Li","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","first-page":"1109","article-title":"Qtn: Quaternion transformer network for hyperspectral image classification","volume":"10","author":"Yang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_48","first-page":"1","article-title":"A center-masked transformer for hyperspectral image classification","volume":"62","author":"Jia","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2024.3490534","article-title":"WaveFormer: Spectral\u2013spatial wavelet transformer for hyperspectral image classification","volume":"21","author":"Ahmad","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","first-page":"1","article-title":"Hyperspectral image classification using groupwise separable convolutional vision transformer network","volume":"62","author":"Zhao","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Huang, K., Deng, X., Geng, J., and Jiang, W. (2021, January 11\u201316). Self-attention and mutual-attention for few-shot hyperspectral image classification. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554361"},{"key":"ref_52","first-page":"1","article-title":"Double attention transformer for hyperspectral image classification","volume":"20","author":"Tang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","unstructured":"Nie, F., Huang, H., Ding, C., Luo, D., and Wang, H. (2011, January 16\u201322). Robust principal component analysis with non-greedy L1-norm maximization. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, Spain."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"93","DOI":"10.5566\/ias.2928","article-title":"Noise robust hyperspectral image classification with MNF-based edge preserving features","volume":"42","author":"Chen","year":"2023","journal-title":"Image Anal. Stereol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"26199","DOI":"10.1109\/ACCESS.2023.3255990","article-title":"BiLSTM model with attention mechanism for sentiment classification on Chinese mixed text comments","volume":"11","author":"Li","year":"2023","journal-title":"IEEE Access"},{"key":"ref_56","first-page":"0196","article-title":"GTFN: GCN and transformer fusion with spatial-spectral features for hyperspectral image classification","volume":"61","author":"Yang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3124\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:42:46Z","timestamp":1760110966000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3124"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":56,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173124"],"URL":"https:\/\/doi.org\/10.3390\/rs16173124","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,8,24]]}}}