{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T11:29:21Z","timestamp":1763724561991,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T00:00:00Z","timestamp":1637971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["61902313"],"award-info":[{"award-number":["61902313"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multi-sensor image can provide supplementary information, usually leading to better performance in classification tasks. However, the general deep neural network-based multi-sensor classification method learns each sensor image separately, followed by a stacked concentrate for feature fusion. This way requires a large time cost for network training, and insufficient feature fusion may cause. Considering efficient multi-sensor feature extraction and fusion with a lightweight network, this paper proposes an attention-guided classification method (AGCNet), especially for multispectral (MS) and panchromatic (PAN) image classification. In the proposed method, a share-split network (SSNet) including a shared branch and multiple split branches performs feature extraction for each sensor image, where the shared branch learns basis features of MS and PAN images with fewer learn-able parameters, and the split branch extracts the privileged features of each sensor image via multiple task-specific attention units. Furthermore, a selective classification network (SCNet) with a selective kernel unit is used for adaptive feature fusion. The proposed AGCNet can be trained by an end-to-end fashion without manual intervention. The experimental results are reported on four MS and PAN datasets, and compared with state-of-the-art methods. The classification maps and accuracies show the superiority of the proposed AGCNet model.<\/jats:p>","DOI":"10.3390\/rs13234823","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4823","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Attention-Guided Multispectral and Panchromatic Image Classification"],"prefix":"10.3390","volume":"13","author":[{"given":"Cheng","family":"Shi","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Yenan","family":"Dang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0969-4083","authenticated-orcid":false,"given":"Li","family":"Fang","sequence":"additional","affiliation":[{"name":"The Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362000, China"}]},{"given":"Zhiyong","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Huifang","family":"Shen","sequence":"additional","affiliation":[{"name":"The Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362000, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4689","DOI":"10.1109\/TGRS.2020.3022608","article-title":"Unifying top\u2013down views by task-specific domain adaptation","volume":"59","author":"Lin","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lin, J., Qi, W., and Yuan, Y. (2014, January 14\u201318). In defense of iterated conditional mode for hyperspectral image classification. Proceedings of the 2014 IEEE International Conference on Multimedia and Expo (ICME), Chengdu, China.","DOI":"10.1109\/ICME.2014.6890171"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lv, Y., Liu, T.F., Benediktsson, J.A., and Falco, N. (2021). Land cover change detection techniques: Very-high-resolution optical images: A review. IEEE Trans. Remote Sens. Mag.","DOI":"10.1109\/MGRS.2021.3088865"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.1109\/LGRS.2020.2998684","article-title":"Local histogram-based analysis for detecting land cover change using VHR remote sensing images","volume":"18","author":"Lv","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wu, Y., Huang, M., Li, Y., Feng, S., and Wu, D. (2021). A Distributed Fusion Framework of Multispectral and Panchromatic Images Based on Residual Network. Remote Sens., 13.","DOI":"10.3390\/rs13132556"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4141","DOI":"10.1109\/TGRS.2017.2689018","article-title":"Superpixel-Based Multiple Local CNN for Panchromatic and Multispectral Image Classification","volume":"55","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Feng, J., Li, D., Gu, J., Cao, X., and Jiao, L. (2021). Deep reinforcement learning for semisupervised hyperspectral band selection. IEEE Trans. Geosci. Remote Sens., 1\u201319.","DOI":"10.1109\/TGRS.2021.3049372"},{"key":"ref_8","first-page":"108316","article-title":"Explainable scale distillation for hyperspectral image classification","volume":"122","author":"Cheng","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2581","DOI":"10.1109\/TPAMI.2019.2929038","article-title":"Learning with privileged information via adversarial discriminative modality distillation","volume":"42","author":"Garcia","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Li, J., Shao, W., Peng, Z., Zhang, R., Wang, X., and Luo, P. (2019, January 27\u201328). Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks. Proceedings of the International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00364"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, X., Kan, M., Shan, S., and Chen, X. (2019, January 16\u201320). Fully Learnable Group Convolution for Acceleration of Deep Neural Networks. Proceedings of the Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00926"},{"key":"ref_12","unstructured":"Howard, A., Chen, B., Kalenichenko, D., Weyand, T., Zhu, M., Andreetto, M., and Wang, W. (2019, January 16\u201320). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Proceedings of the Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA."},{"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":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019, January 16\u201320). Selective Kernel Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4048","DOI":"10.1109\/JSTARS.2018.2874225","article-title":"Active-learning-incorporated deep transfer learning for hyperspectral image classification","volume":"11","author":"Lin","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lin, D., Lin, J., Zhao, L., Wang, Z.J., and Chen, Z. (2021). Multilabel aerial image classification with a concept attention graph neural network. IEEE Trans. Geosci. Remote Sens., 1\u201312.","DOI":"10.1109\/TGRS.2020.3041461"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4073","DOI":"10.1109\/JSTARS.2016.2517204","article-title":"Spectral\u2013Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder","volume":"9","author":"Ma","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2693","DOI":"10.1109\/TGRS.2017.2651639","article-title":"Self-Taught Feature Learning for Hyperspectral Image Classification","volume":"55","author":"Kemker","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral\u2013Spatial Classification of Hyperspectral Data Based on Deep Belief Network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.patcog.2016.10.019","article-title":"Hyperspectral image reconstruction by deep convolutional neural network for classification","volume":"63","author":"Li","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lu, Y., Xie, K., Xu, G., Dong, H., Li, C., and Li, T. (2020). MTFC: A Multi-GPU Training Framework for Cube-CNN-based Hyperspectral Image Classification. IEEE Trans. Emerg. Top. Comput.","DOI":"10.1109\/TETC.2020.3016978"},{"key":"ref_22","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","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lei, T., Li, L., Lv, Z., Zhu, M., Du, X., and Nandi, A.K. (2021). Multi-modality and multi-scale attention fusion network for land cover classification from VHR remote sensing images. Remote Sens., 13.","DOI":"10.3390\/rs13183771"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2461","DOI":"10.1109\/TGRS.2020.2999957","article-title":"Adaptive Spectral-Spatial Multiscale Contextual Feature Extraction for Hyperspectral Image Classification","volume":"59","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1109\/TIP.2018.2809606","article-title":"Diverse Region-Based CNN for Hyperspectral Image Classification","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5046","DOI":"10.1109\/TGRS.2018.2805286","article-title":"Generative Adversarial Networks for Hyperspectral Image Classification","volume":"56","author":"Lin","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","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","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5329","DOI":"10.1109\/TGRS.2019.2899057","article-title":"Classification of Hyperspectral Images Based on Multiclass Spatial\u2013Spectral Generative Adversarial Networks","volume":"57","author":"Feng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lin, J., Mou, L., Yu, T., Zhu, X., and Wang, Z.J. (2020, January 12\u201316). Dual adversarial network for unsupervised ground\/satellite-to-aerial scene adaptation. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","DOI":"10.1145\/3394171.3413893"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/JSTARS.2019.2911113","article-title":"Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest","volume":"12","author":"Xu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/TGRS.2019.2938724","article-title":"Spectral Image Classification From Multi-Sensor Compressive Measurements","volume":"58","author":"Ramirez","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hinojosa, C., Ramirez, J., and Arguello, H. (2019, January 22\u201325). Spectral-Spatial Classification from Multi-Sensor Compressive Measurements Using Superpixels. Proceedings of the IEEE International Conference on Image Processing, Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803266"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1109\/TGRS.2017.2750220","article-title":"Deep Multiple Instance Learning-Based Spatial\u2013Spectral Classification for PAN and MS Imagery","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.ins.2020.06.011","article-title":"Two-stream feature aggregation deep neural network for scene classification of remote sensing images","volume":"539","author":"Xu","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"71353","DOI":"10.1109\/ACCESS.2020.2986267","article-title":"Small Sample Classification of Hyperspectral Remote Sensing Images Based on Sequential Joint Deeping Learning Model","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Feng, J., Feng, X., Chen, J., Cao, X., and Yu, T. (2020). Generative adversarial networks based on collaborative learning and attention mechanism for hyperspectral image classification. Remote Sens., 12.","DOI":"10.3390\/rs12071149"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5336","DOI":"10.1109\/TGRS.2020.2963848","article-title":"Dimensionality Reduction with Enhanced Hybrid-Graph Discriminant Learning for Hyperspectral Image Classification","volume":"58","author":"Luo","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bell, S., Zitnick, C., Bala, K., and Girshick, R. (2016, January 27\u201330). Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks. Proceedings of the Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.314"},{"key":"ref_39","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K. (2015, January 7\u201312). Spatial Transformer Networks. Proceedings of the Annual Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., and Deng, J. (2016, January 11\u201314). Stacked hourglass net-works for human pose estimation. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"107448","DOI":"10.1016\/j.patcog.2020.107448","article-title":"Multistage attention network for image inpainting","volume":"106","author":"Wang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6344","DOI":"10.1109\/ACCESS.2019.2963769","article-title":"Scene Classification of Remote Sensing Images Based on Saliency Dual Attention Residual Network","volume":"8","author":"Guo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4121","DOI":"10.1109\/JSTARS.2020.3009352","article-title":"Channel-Attention-Based DenseNet Network for Remote Sensing Image Scene Classification","volume":"13","author":"Tong","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_44","first-page":"4047","article-title":"Deep Learning for Multilabel Remote Sensing Image Annotation with Dual-Level Semantic Concepts","volume":"58","author":"Zhu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/LGRS.2018.2872355","article-title":"Multiscale Visual Attention Networks for Object Detection in VHR Remote Sensing Images","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1926","DOI":"10.1109\/LGRS.2020.3011405","article-title":"Remote Sensing Image Scene Classification Based on an Enhanced Attention Module","volume":"18","author":"Zhao","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.neucom.2020.04.036","article-title":"Xnet: Task-specific attentional domain adaptation for satellite-to-aerial scene","volume":"406","author":"Lin","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5277","DOI":"10.1109\/TGRS.2019.2961681","article-title":"Lightweight Spectral-Spatial Squeeze-and-Excitation Residual Bag-of-Features Learning for Hyperspectral Classification","volume":"58","author":"Roy","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.inffus.2020.12.008","article-title":"A spatial-channel progressive fusion ResNet for remote sensing classification","volume":"70","author":"Zhu","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"77060","DOI":"10.1109\/ACCESS.2020.2989428","article-title":"Multi-task learning model based on Multi-scale CNN and LSTM for sentiment classification","volume":"8","author":"Jin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_51","unstructured":"Cavallaro, G., Bazi, Y., Melgani, F., and Riedel, M. (August, January 28). Multi-Scale Convolutional SVM Networks for Multi-Class Classification Problems of Remote Sensing Images. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"112265","DOI":"10.1016\/j.rse.2020.112265","article-title":"An automated, generalized, deep-learning-based method for delineating the calving fronts of Greenland glaciers from multi-sensor remote sensing imagery","volume":"254","author":"Zhang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_53","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_54","unstructured":"(2021, November 01). NYU Computer Science. Available online: https:\/\/cs.nyu.edu\/home\/index.html."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TGRS.2004.842478","article-title":"Classification of hyperspectral data from urban areas based on extended morphological profiles","volume":"43","author":"Benediktsson","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MGRS.2020.2979764","article-title":"Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep: Overview and Toolbox","volume":"8","author":"Rasti","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4823\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:36:49Z","timestamp":1760168209000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4823"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,27]]},"references-count":56,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234823"],"URL":"https:\/\/doi.org\/10.3390\/rs13234823","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,11,27]]}}}