{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:40:06Z","timestamp":1760233206872,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T00:00:00Z","timestamp":1672444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Projects","award":["2022YFF0904400","41830108"],"award-info":[{"award-number":["2022YFF0904400","41830108"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022YFF0904400","41830108"],"award-info":[{"award-number":["2022YFF0904400","41830108"]}],"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>Change detection methods using hyperspectral remote sensing can precisely identify differences of the same area at different observing times. However, due to massive spectral bands, current change detection methods are vulnerable to unrelatedspectral and spatial information in hyperspectral images with the stagewise calculation of attention maps. Besides, current change methods arrange hidden change features in a random distribution form, which cannot express a class-oriented discrimination in advance. Moreover, existent deep change methods have not fully considered the hierarchical features\u2019 reuse and the fusion of the encoder\u2013decoder framework. To better handle the mentioned existent problems, the parallel spectral\u2013spatial attention network with feature redistribution loss (TFR-PS2ANet) is proposed. The contributions of this article are summarized as follows: (1) a parallel spectral\u2013spatial attention module (PS2A) is introduced to enhance relevant information and suppress irrelevant information in parallel using spectral and spatial attention maps extracted from the original hyperspectral image patches; (2) the feature redistribution loss function (FRL) is introduced to construct the class-oriented feature distribution, which organizes the change features in advance and improves the discriminative abilities; (3) a two-branch encoder\u2013decoder framework is developed to optimize the hierarchical transfer and change features\u2019 fusion; Extensive experiments were carried out on several real datasets. The results show that the proposed PS2A can enhance significant information effectively and the FRL can optimize the class-oriented feature distribution. The proposed method outperforms most existent change detection methods.<\/jats:p>","DOI":"10.3390\/rs15010246","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T03:00:59Z","timestamp":1672628459000},"page":"246","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Parallel Spectral\u2013Spatial Attention Network with Feature Redistribution Loss for Hyperspectral Change Detection"],"prefix":"10.3390","volume":"15","author":[{"given":"Yixiang","family":"Huang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, No. 3 Datun Road, Chaoyang District, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3533-9966","authenticated-orcid":false,"given":"Lifu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 100101, China"}]},{"given":"Changping","family":"Huang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 100101, China"}]},{"given":"Wenchao","family":"Qi","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7872-5587","authenticated-orcid":false,"given":"Ruoxi","family":"Song","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","article-title":"Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Du, P., Liu, S., Bruzzone, L., and Bovolo, F. (2012, January 22\u201327). Target-Driven Change Detection Based on Data Transformation and Similarity Measures. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6350981"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kaldane, H., Turkar, V., De, S., Shitole, S., and Deo, R. (2019, January 9\u201315). Land Cover Change Detection for Fully Polarimetric SAR Images. Proceedings of the 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC), New Delhi, India.","DOI":"10.23919\/URSIAP-RASC.2019.8738320"},{"key":"ref_4","first-page":"102492","article-title":"Direction-Dominated Change Vector Analysis for Forest Change Detection","volume":"103","author":"Xiao","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Washaya, P., Balz, T., and Mohamadi, B. (2018). Coherence Change-Detection with Sentinel-1 for Natural and Anthropogenic Disaster Monitoring in Urban Areas. Remote Sens., 10.","DOI":"10.3390\/rs10071026"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/MGRS.2019.2898520","article-title":"A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1109\/TGRS.2014.2321277","article-title":"Peijun Du Hierarchical Unsupervised Change Detection in Multitemporal Hyperspectral Images","volume":"53","author":"Liu","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/TGRS.2011.2171493","article-title":"A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images","volume":"50","author":"Bovolo","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1080\/22797254.2017.1367963","article-title":"A New Land-Cover Match-Based Change Detection for Hyperspectral Imagery","volume":"50","author":"Seydi","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.neucom.2021.08.130","article-title":"Spectral Mapping with Adversarial Learning for Unsupervised Hyperspectral Change Detection","volume":"465","author":"Lei","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_11","first-page":"1","article-title":"A Spectral and Spatial Attention Network for Change Detection in Hyperspectral Images","volume":"60","author":"Gong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3156041","article-title":"A CNN Framework With Slow-Fast Band Selection and Feature Fusion Grouping for Hyperspectral Image Change Detection","volume":"60","author":"Ou","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"110137","DOI":"10.1016\/j.measurement.2021.110137","article-title":"A New Structure for Binary and Multiple Hyperspectral Change Detection Based on Spectral Unmixing and Convolutional Neural Network","volume":"186","author":"Seydi","year":"2021","journal-title":"Measurement"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zuobin, W., Kezhi, M., and Ng, G.-W. (2018, January 10\u201313). Feature Regrouping for CCA-Based Feature Fusion and Extraction Through Normalized Cut. Proceedings of the 2018 21st International Conference on Information Fusion, Cambridge, UK.","DOI":"10.23919\/ICIF.2018.8455397"},{"key":"ref_15","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv."},{"key":"ref_16","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image Is Worth 16x16 Words: Transformers for Image Recognition At Scale. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-End Object Detection with Transformers. arXiv.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., and Dong, L. (2022). Swin Transformer V2: Scaling Up Capacity and Resolution. arXiv.","DOI":"10.1109\/CVPR52688.2022.01170"},{"key":"ref_19","first-page":"1","article-title":"Change Detection in Synthetic Aperture Radar Images Using a Dual-Domain Network","volume":"19","author":"Qu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, G., Peng, Y., Zhang, S., Wang, G., Zhang, T., Qi, J., Zheng, S., and Liu, Y. (2021). Pyramid Self-Attention Mechanism-Based Change Detection in Hyperspectral Imagery. J. Appl. Remote Sens., 15.","DOI":"10.1117\/1.JRS.15.042611"},{"key":"ref_21","first-page":"18","article-title":"SSA-SiamNet: Spectral\u2013Spatial-Wise Attention-Based Siamese Network for Hyperspectral Image Change Detection","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","unstructured":"Bao, H., Dong, L., and Wei, F. (2021). BEiT: BERT Pre-Training of Image Transformers. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Strudel, R., Garcia, R., Laptev, I., and Schmid, C. (2021, January 10\u201317). Segmenter: Transformer for Semantic Segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Takase, S., and Kiyono, S. (2021). Rethinking Perturbations in Encoder-Decoders for Fast Training. arXiv.","DOI":"10.18653\/v1\/2021.naacl-main.460"},{"key":"ref_25","unstructured":"Sutskever, I., Vinyals, O., and Le, Q.V. (2014, January 8\u201313). Sequence to Sequence Learning with Neural Networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2018.2849692","article-title":"GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9976","DOI":"10.1109\/TGRS.2019.2930682","article-title":"Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images","volume":"57","author":"Du","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","unstructured":"Zhu, Q., Deng, W., Zheng, Z., Zhong, Y., Guan, Q., Lin, W., Zhang, L., and Li, D. (2021). A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification. IEEE Trans. Cybern., 1\u201315."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Miao, X., Yuan, X., Pu, Y., and Athitsos, V. (November, January 27). Lambda-Net: Reconstruct Hyperspectral Images From a Snapshot Measurement. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00416"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, T., Dong, W., Yuan, X., Wu, J., and Shi, G. (2021, January 19\u201325). Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01595"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cai, Y., Lin, J., Hu, X., Wang, H., Yuan, X., Zhang, Y., Timofte, R., and Van Gool, L. (2022, January 23\u201327). Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19790-1_41"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, Q., Mu, T., Feng, Y., Gong, H., Han, F., Tuniyazi, A., Li, H., Wang, W., Li, C., and He, Z. (2021, January 15). Hyperspectral Image Change Detection Using Two-Branch Unet Network with Feature Fusion. Proceedings of the Fourth International Conference on Photonics and Optical Engineering, Xi\u2019an, China.","DOI":"10.1117\/12.2586808"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7029","DOI":"10.1080\/01431161.2018.1466079","article-title":"Hyperspectral Change Detection: An Experimental Comparative Study","volume":"39","author":"Hasanlou","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1109\/TGRS.2006.885408","article-title":"A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain","volume":"45","author":"Bovolo","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.isprsjprs.2006.09.004","article-title":"Multiple Support Vector Machines for Land Cover Change Detection: An Application for Mapping Urban Extensions","volume":"61","author":"Nemmour","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/246\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:49:24Z","timestamp":1760147364000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/246"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,31]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010246"],"URL":"https:\/\/doi.org\/10.3390\/rs15010246","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,12,31]]}}}