{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:21:39Z","timestamp":1760149299045,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3104301"],"award-info":[{"award-number":["2022YFC3104301"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Environmental images that are captured by satellites can provide significant information for weather forecasting, climate warning, and so on. This article introduces a novel deep neural network that integrates a convolutional attention feature extractor (CAFE) in a recurrent neural network frame and a multivariate dynamic time warping (MDTW) loss. The CAFE module is designed to capture the complicated and hidden dependencies within image series between the source variable and the target variable. The proposed method can reconstruct the image series across environmental variables. The performance of the proposed method is validated by experiments using a real-world remote sensing dataset and compared with several representative methods. Experimental results demonstrate the emerging performance of the proposed method for cross-variable image series reconstruction.<\/jats:p>","DOI":"10.3390\/rs15143552","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:56:47Z","timestamp":1689555407000},"page":"3552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Convolution and Attention Neural Network with MDTW Loss for Cross-Variable Reconstruction of Remote Sensing Image Series"],"prefix":"10.3390","volume":"15","author":[{"given":"Chao","family":"Li","sequence":"first","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan 250061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinglei","family":"Su","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunlin","family":"Ning","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao 266061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan 250061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/LGRS.2014.2348651","article-title":"Reconstructing MODIS LST based on multitemporal classification and robust regression","volume":"12","author":"Zeng","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.isprsjprs.2018.04.005","article-title":"A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud","volume":"141","author":"Zeng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.isprsjprs.2018.06.008","article-title":"Comprehensive assessment of four-parameter diurnal land surface temperature cycle models under clear-sky","volume":"142","author":"Hong","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.3390\/rs15051454","article-title":"A Comprehensive Survey on SAR ATR in Deep-Learning Era","volume":"15","author":"Li","year":"2023","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_9","first-page":"1","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume":"28","author":"Shi","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_10","first-page":"1","article-title":"Deep learning for precipitation nowcasting: A benchmark and a new model","volume":"30","author":"Shi","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1109\/TGRS.2017.2777886","article-title":"Reconstructing cloud-contaminated multispectral images with contextualized autoencoder neural networks","volume":"56","author":"Malek","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4274","DOI":"10.1109\/TGRS.2018.2810208","article-title":"Missing data reconstruction in remote sensing image with a unified spatial\u2013temporal\u2013spectral deep convolutional neural network","volume":"56","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","first-page":"1","article-title":"Progressive spatial\u2013spectral joint network for hyperspectral image reconstruction","volume":"60","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3719","DOI":"10.1109\/JSTARS.2021.3068274","article-title":"Attention-based tri-UNet for remote sensing image pan-sharpening","volume":"14","author":"Zhang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3146246","article-title":"SEN12MS-CR-TS: A remote-sensing data set for multimodal multitemporal cloud removal","volume":"60","author":"Ebel","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TAC.1959.1104847","article-title":"On adaptive control processes","volume":"4","author":"Bellman","year":"1959","journal-title":"IRE Trans. Autom. Control."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2022.02.016","article-title":"The effects of Landsat image acquisition date on winter wheat classification in the North China Plain","volume":"187","author":"Fan","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4297","DOI":"10.1109\/LRA.2021.3067623","article-title":"STA-VPR: Spatio-temporal alignment for visual place recognition","volume":"6","author":"Lu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_19","first-page":"2","article-title":"Dtwnet: A dynamic time warping network","volume":"32","author":"Cai","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","unstructured":"Cuturi, M., and Blondel, M. (2017, January 20\u201322). Soft-dtw: A differentiable loss function for time-series. Proceedings of the International Conference on Machine Learning PMLR, New York, NY, USA."},{"key":"ref_21","first-page":"1","article-title":"Lower-bounding of dynamic time warping distances for multivariate time series","volume":"40","author":"Rath","year":"2002","journal-title":"Univ. Mass. Amherst Tech. Rep. MM"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10618-016-0455-0","article-title":"Generalizing DTW to the multi-dimensional case requires an adaptive approach","volume":"31","author":"Hu","year":"2017","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, X., Gao, Y., Lin, J., and Lu, C.T. (2020, January 3). Tapnet: Multivariate time series classification with attentional prototypical network. Proceedings of the AAAI Conference on Artificial Intelligence, Online.","DOI":"10.1609\/aaai.v34i04.6165"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.ins.2022.11.082","article-title":"TC-DTW: Accelerating multivariate dynamic time warping through triangle inequality and point clustering","volume":"621","author":"Shen","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, H., Wan, J., Liu, S., Sheng, H., and Xu, M. (2022). Wetland Vegetation Classification through Multi-Dimensional Feature Time Series Remote Sensing Images Using Mahalanobis Distance-Based Dynamic Time Warping. Remote Sens., 14.","DOI":"10.3390\/rs14030501"},{"key":"ref_26","unstructured":"(2023, May 22). The Regional NCOM AMSEAS 2D Dataset, Available online: https:\/\/www.ncei.noaa.gov\/erddap\/griddap\/NCOM_amseas_latest2d.html."},{"key":"ref_27","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3552\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:12:23Z","timestamp":1760127143000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3552"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,14]]},"references-count":27,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143552"],"URL":"https:\/\/doi.org\/10.3390\/rs15143552","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,7,14]]}}}