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Despite the remarkable success of DL, the burden of a manually designed DL structure with increased depth and size aroused the difficulty for the application in the mobile and embedded devices in a real application. To tackle this issue, in this paper, we proposed an efficient lightweight attention network architecture search algorithm (EL-NAS) for realizing an efficient automatic design of a lightweight DL structure as well as improving the classification performance of HSI. First, aimed at realizing an efficient search procedure, we construct EL-NAS based on a differentiable network architecture search (NAS), which can greatly accelerate the convergence of the over-parameter supernet in a gradient descent manner. Second, in order to realize lightweight search results with high accuracy, a lightweight attention module search space is designed for EL-NAS. Finally, further for alleviating the problem of higher validation accuracy and worse classification performance, the edge decision strategy is exploited to perform edge decisions through the entropy of distribution estimated over non-skip operations to avoid further performance collapse caused by numerous skip operations. To verify the effectiveness of EL-NAS, we conducted experiments on several real-world hyperspectral images. The results demonstrate that the proposed EL-NAS indicates a more efficient search procedure with smaller parameter sizes and high accuracy performance for HSI classification, even under data-independent and sensor-independent scenarios.<\/jats:p>","DOI":"10.3390\/rs15194688","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T02:31:29Z","timestamp":1695695489000},"page":"4688","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["EL-NAS: Efficient Lightweight Attention Cross-Domain Architecture Search for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6704-1198","authenticated-orcid":false,"given":"Jianing","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Computer Science and Technology, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710071, China"}]},{"given":"Jinyu","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710071, China"}]},{"given":"Yichen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710071, China"}]},{"given":"Zheng","family":"Hua","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710071, China"}]},{"given":"Shengjia","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710071, China"}]},{"given":"Yuqiong","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,25]]},"reference":[{"key":"ref_1","unstructured":"Lacar, F.M., Lewis, M.M., and Grierson, I.T. (2001, January 9\u201313). Use of hyperspectral imagery for mapping grape varieties in the Barossa Valley, South Australia. Proceedings of the Geoscience and Remote Sensing Symposium, Sydney, Australia."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral Remote Sensing Data Analysis and Future Challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2019.04.017","article-title":"Social sensing from street-level imagery: A case study in learning spatio-temporal urban mobility patterns","volume":"153","author":"Zhang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4955","DOI":"10.1109\/TGRS.2013.2286195","article-title":"Hyperspectral remote sensing image subpixel target detection based on supervised metric learning","volume":"52","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MGRS.2018.2867592","article-title":"Mini-UAV-Borne Hyperspectral Remote Sensing: From Observation and Processing to Applications","volume":"6","author":"Zhong","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/TGRS.2012.2201730","article-title":"Hyperspectral image classification via kernel sparse representation","volume":"51","author":"Chen","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","unstructured":"Yi, C., Nasrabadi, N.M., and Tran, T.D. (2010, January 14\u201316). Classification for hyperspectral imagery based on sparse representation. Proceedings of the Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Reykjavik, Iceland."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"4810","DOI":"10.1109\/TGRS.2015.2410991","article-title":"Region-Kernel-Based Support Vector Machines for Hyperspectral Image Classification","volume":"53","author":"Peng","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1109\/JSTARS.2015.2394330","article-title":"Hyperspectral Image Classification by Spatial\u2013Spectral Derivative-Aided Kernel Joint Sparse Representation","volume":"8","author":"Wang","year":"2015","journal-title":"Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTARS.2016.2526604","article-title":"Adaptive Nonlocal Spatial\u2013Spectral Kernel for Hyperspectral Imagery Classification","volume":"9","author":"Wang","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","first-page":"563","article-title":"Recent advances in deep learning","volume":"57","author":"Saxena","year":"2016","journal-title":"Comput. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1080\/2150704X.2017.1280200","article-title":"Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network","volume":"8","author":"Zhang","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_15","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_16","unstructured":"Slavkovikj, V., Verstockt, S., Neve, W.D., Hoecke, S.V., and Walle, R. (2021, January 18\u201322). Hyperspectral Image Classification with Convolutional Neural Networks. Proceedings of the the 23rd ACM International Conference, Montreal, QC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"He, M., Bo, L., and Chen, H. (2017, January 17\u201320). Multi-scale 3D deep convolutional neural network for hyperspectral image classification. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8297014"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TGRS.2017.2748160","article-title":"Unsupervised Spectral-Spatial Feature Learning via Deep Residual Conv-Deconv Network for Hyperspectral Image Classification","volume":"56","author":"Mou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1109\/TCSVT.2022.3215513","article-title":"DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery","volume":"33","author":"Xi","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5813","DOI":"10.1109\/TGRS.2019.2902568","article-title":"Hyperspectral Classification Based on Lightweight 3-D-CNN with Transfer Learning","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"1","article-title":"Dual-Channel Capsule Generation Adversarial Network for Hyperspectral Image Classification","volume":"60","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, X., Cao, W., Lu, Y., and Zhou, Y. (2023). QTN: Quaternion Transformer Network for Hyperspectral Image Classification. IEEE Trans. Circuits Syst. Video Technol.","DOI":"10.1109\/TCSVT.2023.3283289"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8754","DOI":"10.1109\/TGRS.2021.3049377","article-title":"NAS-Guided Lightweight Multiscale Attention Fusion Network for Hyperspectral Image Classification","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., and Dollar, P. (2020, January 18\u201324). Designing Network Design Spaces. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"026028","DOI":"10.1117\/1.JRS.12.026028","article-title":"Deep convolutional recurrent neural network with transfer learning for hyperspectral image classification","volume":"12","author":"Liu","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3246","DOI":"10.1109\/TGRS.2019.2951445","article-title":"Heterogeneous transfer learning for hyperspectral image classification based on convolutional neural network","volume":"58","author":"He","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3892","DOI":"10.1109\/JSTARS.2020.3006879","article-title":"Extreme learning machine-based ensemble transfer learning for hyperspectral image classification","volume":"13","author":"Liu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jaderberg, M., Vedaldi, A., and Zisserman, A. (2014). Speeding up Convolutional Neural Networks with Low Rank Expansions. arXiv.","DOI":"10.5244\/C.28.88"},{"key":"ref_30","first-page":"38","article-title":"Distilling the Knowledge in a Neural Network","volume":"14","author":"Hinton","year":"2015","journal-title":"Comput. Sci."},{"key":"ref_31","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., and Zhang, L. (2021). Cvt: Introducing convolutions to vision transformers. arXiv.","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"ref_34","unstructured":"Leiva-Aravena, E., Leiva, E., Zamorano, V., Rojas, C., and John, M. (2019). Neural Architecture Search with Reinforcement Learning. arXiv."},{"key":"ref_35","unstructured":"Pham, H., Guan, M., Zoph, B., Le, Q., and Dean, J. (2018, January 10\u201315). Efficient neural architecture search via parameters sharing. Proceedings of the International Conference on Machine Learning, PMLR, Stockholm, Sweden."},{"key":"ref_36","unstructured":"Baker, B., Gupta, O., Naik, N., and Raskar, R. (2016). Designing neural network architectures using reinforcement learning. arXiv."},{"key":"ref_37","unstructured":"Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y.L., Tan, J., Le, Q.V., and Kurakin, A. (2017, January 20\u201322). Large-scale evolution of image classifiers. Proceedings of the International Conference on Machine Learning, PMLR, Fort Lauderdale, FL, USA."},{"key":"ref_38","unstructured":"Liu, H., Simonyan, K., Vinyals, O., Fernando, C., and Kavukcuoglu, K. (2017). Hierarchical representations for efficient architecture search. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.V. (2018, January 18\u201323). Learning transferable architectures for scalable image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q.V. (2019, January 15\u201320). Mnasnet: Platform-aware neural architecture search for mobile. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00293"},{"key":"ref_41","unstructured":"Liu, H., Simonyan, K., and Yang, Y. (2018). Darts: Differentiable architecture search. arXiv."},{"key":"ref_42","unstructured":"Li, C., Ning, J., Hu, H., and He, K. (2022). Enhancing the Robustness, Efficiency, and Diversity of Differentiable Architecture Search. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Xia, X., Xiao, X., Wang, X., and Zheng, M. (2022, January 3\u20138). Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00358"},{"key":"ref_44","unstructured":"Liu, Y., Li, T., Zhang, P., and Yan, Y. (2021). Improved conformer-based end-to-end speech recognition using neural architecture search. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, H., Wu, G., and Zheng, W.S. (2021, January 20\u201325). Combined depth space based architecture search for person re-identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00666"},{"key":"ref_46","first-page":"1","article-title":"3-D-ANAS: 3-D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xue, X., Zhang, H., Fang, B., Bai, Z., and Li, Y. (2021). Grafting Transformer Module on Automatically Designed ConvNet for Hyperspectral Image Classification. arXiv.","DOI":"10.1109\/TGRS.2022.3180685"},{"key":"ref_48","unstructured":"Liang, H., Zhang, S., Sun, J., He, X., Huang, W., Zhuang, K., and Li, Z. (2019). Darts+: Improved differentiable architecture search with early stopping. arXiv."},{"key":"ref_49","unstructured":"Xu, Y., Xie, L., Zhang, X., Chen, X., Qi, G.J., Tian, Q., and Xiong, H. (2019). PC-DARTS: Partial channel connections for memory-efficient architecture search. arXiv."},{"key":"ref_50","unstructured":"Chu, X., Wang, X., Zhang, B., Lu, S., Wei, X., and Yan, J. (2020). DARTS-: Robustly stepping out of performance collapse without indicators. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Li, G., Qian, G., Delgadillo, I.C., Muller, M., Thabet, A., and Ghanem, B. (2020, January 13\u201319). Sgas: Sequential greedy architecture search. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00169"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chu, X., Zhang, B., and Xu, R. (2021, January 11\u201317). Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01202"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hou, P., Jin, Y., and Chen, Y. (2021, January 20\u201325). Single-DARTS: Towards Stable Architecture Search. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Nashville, TN, USA.","DOI":"10.1109\/ICCVW54120.2021.00046"},{"key":"ref_54","unstructured":"Zela, A., Elsken, T., Saikia, T., Marrakchi, Y., Brox, T., and Hutter, F. (2019). Understanding and Robustifying Differentiable Architecture Search. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ye, P., Li, B., Li, Y., Chen, T., Fan, J., and Ouyang, W. (2022). beta-DARTS: Beta-Decay Regularization for Differentiable Architecture Search. arXiv.","DOI":"10.1109\/CVPR52688.2022.01060"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.ins.2023.01.129","article-title":"U-DARTS: Uniform-space differentiable architecture search","volume":"628","author":"Huang","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"109193","DOI":"10.1016\/j.patcog.2022.109193","article-title":"FP-DARTS: Fast parallel differentiable neural architecture search for image classification","volume":"136","author":"Wang","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhang, C., Liu, X., Wang, G., and Cai, Z. (October, January 26). Particle Swarm Optimization Based Deep Learning Architecture Search for Hyperspectral Image Classification. Proceedings of the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324463"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhang, C., Cai, Z., Yang, J., Zhou, Z., and Gong, X. (2021). Continuous Particle Swarm Optimization-Based Deep Learning Architecture Search for Hyperspectral Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13061082"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"7048","DOI":"10.1109\/TGRS.2019.2910603","article-title":"Automatic design of convolutional neural network for hyperspectral image classification","volume":"57","author":"Chen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zhan, L., Fan, J., Ye, P., and Cao, J. (2023, January 4\u20139). A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search for Hyperspectral Image Classification. Proceedings of the ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10094640"},{"key":"ref_62","first-page":"1","article-title":"Lightweight Multiscale Neural Architecture Search With Spectral\u2013Spatial Attention for Hyperspectral Image Classification","volume":"61","author":"Cao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_64","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 (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4688\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:57:37Z","timestamp":1760129857000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4688"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,25]]},"references-count":64,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194688"],"URL":"https:\/\/doi.org\/10.3390\/rs15194688","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,25]]}}}