{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:47Z","timestamp":1740108047743,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T00:00:00Z","timestamp":1722643200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T00:00:00Z","timestamp":1722643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"DOI":"10.1007\/s00521-024-10186-2","type":"journal-article","created":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T07:02:07Z","timestamp":1722668527000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Orthrus: multi-scale land cover mapping from satellite image time series via 2D encoding and convolutional neural network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0964-8797","authenticated-orcid":false,"given":"Azza","family":"Abidi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8736-3132","authenticated-orcid":false,"given":"Dino","family":"Ienco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5714-7562","authenticated-orcid":false,"given":"Ali","family":"Ben Abbes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9114-5659","authenticated-orcid":false,"given":"Imed Riadh","family":"Farah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,3]]},"reference":[{"key":"10186_CR1","first-page":"2352","volume":"28","author":"B Hosseiny","year":"2022","unstructured":"Hosseiny B, Abdi AM, Jamali S (2022) Urban land use and land cover classification with interpretable machine learning \u2013 a case study using sentinel-2 and auxiliary data. Remote Sens Appl: Soc Environ 28:2352\u20139385","journal-title":"Remote Sens Appl: Soc Environ"},{"key":"10186_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cscee.2022.100293","volume":"7","author":"MMH Seyam","year":"2023","unstructured":"Seyam MMH, Haque MR, Rahman MM (2023) Identifying the land use land cover (lulc) changes using remote sensing and gis approach: a case study at Bhaluka in Mymensingh, Bangladesh. Case Stud Chem Environ Eng 7:100293","journal-title":"Case Stud Chem Environ Eng"},{"key":"10186_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.regsus.2023.05.001","author":"S Haldar","year":"2023","unstructured":"Haldar S, Mandal S, Bhattacharya Su, Paul S (2023) Dynamicity of Land Use\/Land Cover (LULC) An analysis from peri-urban and rural neighbourhoods of Durgapur Municipal Corporation (DMC) in India. Reg Sustain. https:\/\/doi.org\/10.1016\/j.regsus.2023.05.001","journal-title":"Reg Sustain"},{"key":"10186_CR4","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1080\/2150704X.2021.2001071","volume":"13","author":"A Abidi","year":"2021","unstructured":"Abidi A, Abbes A, Gbodjo Y, Ienco D, Farah I (2021) Combining pixel- and object-level information for land-cover mapping using time-series of Sentinel-2 satellite data. Remote Sens Lett 13:11. https:\/\/doi.org\/10.1080\/2150704X.2021.2001071","journal-title":"Remote Sens Lett"},{"key":"10186_CR5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3024133","author":"D Ienco","year":"2020","unstructured":"Ienco D, Gbodjo Y, Gaetano R, Interdonato R (2020) Weakly supervised learning for land cover mapping of satellite image time series via attention-based CNN. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2020.3024133","journal-title":"IEEE Access"},{"key":"10186_CR6","doi-asserted-by":"publisher","first-page":"110172","DOI":"10.1016\/j.asoc.2023.110172","volume":"137","author":"M Rhif","year":"2023","unstructured":"Rhif M, Abbes A, Martinez B, Farah I (2023) Veg-W2TCN: A parallel hybrid forecasting framework for non-stationary time series using wavelet and temporal convolution network model. Appl Soft Comput 137:110172. https:\/\/doi.org\/10.1016\/j.asoc.2023.110172","journal-title":"Appl Soft Comput"},{"key":"10186_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106152","author":"A Abidi","year":"2023","unstructured":"Abidi A, Ienco D, Abbes A, Farah I (2023) Combining 2D encoding and convolutional neural network to enhance land cover mapping from Satellite Image Time Series. Eng Appl Artif  Intell. https:\/\/doi.org\/10.1016\/j.engappai.2023.106152","journal-title":"Eng Appl Artif Intell"},{"key":"10186_CR8","doi-asserted-by":"publisher","first-page":"523","DOI":"10.3390\/rs11050523","volume":"11","author":"C Pelletier","year":"2019","unstructured":"Pelletier C, Webb G, Petitjean F (2019) Temporal convolutional neural network for the classification of satellite image time series. Remote Sens 11:523. https:\/\/doi.org\/10.3390\/rs11050523","journal-title":"Remote Sens"},{"key":"10186_CR9","doi-asserted-by":"publisher","unstructured":"Chelali M, Kurtz C, Puissant A, Vincent N (2021) Deep-STaR: Classification of image time series based on spatio-temporal representations. Comput Vis Image Underst 208-209:103221. https:\/\/doi.org\/10.1016\/j.cviu.2021.103221","DOI":"10.1016\/j.cviu.2021.103221"},{"key":"10186_CR10","unstructured":"Bertini F, Brand O, Carlier S, Del Bello U, Drusch M, Duca R, Fernandez V, Ferrario C, Ferreira MH, Isola C, Kirschner V, Laberinti P, Lambert M, Mandorlo G, Marcos P, Martimort P, Moon S, Oldeman P, Palomba M, Pineiro J (2012) Sentinel-2 ESA\u2019s Optical High-Resolution Mission for GMES Operational Services. ESA bulletin. Bulletin ASE. European Space Agency. SP-1322"},{"key":"10186_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.30855\/gmbd.2021.01.01","volume":"7","author":"A Buz","year":"2021","unstructured":"Buz A, Demirezen M, Yavanoglu U (2021) A novel approach and application of time series to image transformation methods on classification of underwater objects. Gazi J Eng Sci 7:1\u201311. https:\/\/doi.org\/10.30855\/gmbd.2021.01.01","journal-title":"Gazi J Eng Sci"},{"key":"10186_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2020.3030792","author":"W Deng","year":"2020","unstructured":"Deng W, Xu J (2020) An enhanced MSIQDE algorithm with novel multiple strategies for global optimization problems. IEEE Trans Syst Man Cybern: Syst. https:\/\/doi.org\/10.1109\/TSMC.2020.3030792","journal-title":"IEEE Trans Syst Man Cybern: Syst"},{"key":"10186_CR13","doi-asserted-by":"publisher","unstructured":"Rane N, Achari A, Choudhary S, Giduturi M (2023) Effectiveness and capability of remote sensing (RS) and geographic information systems (GIS): a powerful tool for land use and land cover (LULC) change and accuracy assessment 8:286\u2013295. https:\/\/doi.org\/10.5281\/zenodo.7845418","DOI":"10.5281\/zenodo.7845418"},{"issue":"22","key":"10186_CR14","doi-asserted-by":"publisher","first-page":"3798","DOI":"10.3390\/rs12223798","volume":"12","author":"L Ma","year":"2020","unstructured":"Ma L, Schmitt M, Xiaoxiang Z (2020) Uncertainty analysis of object-based land-cover classification using sentinel-2 time-series data. Remote Sensing 12(22):3798","journal-title":"Remote Sensing"},{"key":"10186_CR15","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3390\/rs11121416","volume":"11","author":"M De Giglio","year":"2019","unstructured":"De Giglio M, Greggio N, Goffo F, Merloni N, Dubbini M, Barbarella M (2019) Comparison of pixel-and object-based classification methods of unmanned aerial vehicle data applied to coastal dune vegetation communities: casal borsetti case study. Remote Sens 11:21. https:\/\/doi.org\/10.3390\/rs11121416","journal-title":"Remote Sens"},{"key":"10186_CR16","doi-asserted-by":"crossref","unstructured":"Blaschke T, Hay G, Kelly M, Lang S, Hofmann P, Addink E, Feitosa R, Meer F, van der Werff H, Van Coillie F, Tiede D (2014) Geographic object-based image analysis \u2013 towards a new paradigm. ISPRS J Photogrammetry Remote Sens","DOI":"10.1016\/j.isprsjprs.2013.09.014"},{"key":"10186_CR17","doi-asserted-by":"publisher","first-page":"513","DOI":"10.3390\/rs12030513","volume":"12","author":"D Derksen","year":"2020","unstructured":"Derksen D, Inglada J, Michel J (2020) Geometry aware evaluation of handcrafted superpixel-based features and convolutional neural networks for land cover mapping using satellite imagery. Remote Sens 12:513. https:\/\/doi.org\/10.3390\/rs12030513","journal-title":"Remote Sens"},{"key":"10186_CR18","doi-asserted-by":"crossref","unstructured":"Wang L, Sousa WP, Gong P (2004) Integration of object-based and pixel-based classification for mapping mangroves with Ikonos imagery. Int J Remote Sens","DOI":"10.1080\/014311602331291215"},{"key":"10186_CR19","unstructured":"Christian V, Iraklis L (2022) Analysis of time series imaging approaches for the application of fault classification of marine systems. In: 32nd European Safety and Reliability Conference"},{"key":"10186_CR20","unstructured":"Xing C, Huimin Z, Shifan S, Yongquan Z, Huayue DWU, Wuquan D (2021) An improved quantum-inspired cooperative co-evolution algorithm with muli-strategy and its application. Expert Syst Applicat"},{"key":"10186_CR21","doi-asserted-by":"crossref","unstructured":"Sagheer A, Kotb M (2019) Unsupervised pre-training of a Deep LSTM-based stacked autoencoder for multivariate time series forecasting problems. Sci Rep","DOI":"10.1038\/s41598-019-55320-6"},{"key":"10186_CR22","doi-asserted-by":"crossref","unstructured":"Menini N, Almeida A, Lamparelli R, le Maire G, dos Santos J, Pedrini H, Hirota M, Torres R (2018) A Soft Computing Framework for Image Classification Based on Recurrence Plots. IEEE Geosci Remote Sens Lett","DOI":"10.1109\/LGRS.2018.2872132"},{"key":"10186_CR23","doi-asserted-by":"crossref","unstructured":"Dias D, Pinto A, Dias U, Rubens L, Maire GL, Torres R (2020) A multi-representational fusion of time series for pixelwise classification. IEEE J Select Topics Appl Earth Observ Remote Sens","DOI":"10.1109\/JSTARS.2020.3012117"},{"key":"10186_CR24","unstructured":"Zhiguang W, Tim O (2015) Imaging time-series to improve classification and imputation. In: IJCAI International Joint Conference on Artificial Intelligence"},{"key":"10186_CR25","doi-asserted-by":"publisher","unstructured":"Bayarri D, Belaire-Franch J (2002) Recurrence plots in nonlinear time series analysis: free software. J Stat Softw. https:\/\/doi.org\/10.18637\/jss.v007.i09","DOI":"10.18637\/jss.v007.i09"},{"key":"10186_CR26","unstructured":"Lung YC, Yi YC, Xuan CZ, Wei LN, Senior M (2019) Multivariate time series data transformation for convolutional neural network. In: Proceedings of the 2019 IEEE\/SICE international symposium on system integration, SII 2019"},{"key":"10186_CR27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3055554","author":"A Censi","year":"2021","unstructured":"Censi A, Ienco D, Gbodjo Y, Pensa R, Interdonato R, Gaetano R (2021) Attentive spatial temporal graph cnn for land cover mapping from multi temporal remote sensing data. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2021.3055554","journal-title":"IEEE Access"},{"key":"10186_CR28","unstructured":"Fare GVS, Loic L (2020) Lightweight temporal self-attention for classifying satellite images time series. In: Lemaire V, Malinowski S, Bagnall A, Guyet T, Tavenard R, Ifrim G (eds) Advanced Analytics and Learning on Temporal Data. Springer"},{"key":"10186_CR29","doi-asserted-by":"publisher","first-page":"618","DOI":"10.3390\/rs15030618","volume":"15","author":"W Zhang","year":"2023","unstructured":"Zhang W, Zhang H, Zhao Z, Tang P, Zhang Z (2023) Attention to both global and local features: a novel temporal encoder for satellite image time series classification. Remote Sens 15:618. https:\/\/doi.org\/10.3390\/rs15030618","journal-title":"Remote Sens"},{"issue":"1","key":"10186_CR30","doi-asserted-by":"publisher","first-page":"45","DOI":"10.3390\/rs15010045","volume":"15","author":"J Wu","year":"2023","unstructured":"Wu J, Xie C, Zhang Z, Zhu Y (2023) A Deeply Supervised Attentive High-Resolution Network for Change Detection in Remote Sensing Images. Remote Sens 15(1):45","journal-title":"Remote Sens"},{"key":"10186_CR31","doi-asserted-by":"crossref","unstructured":"Derksen D, Inglada J, Michel J (2018) Spatially precise contextual features based on superpixel neighborhoods for land cover mapping with high resolution satellite image time series 200-203.","DOI":"10.1109\/IGARSS.2018.8518961"},{"key":"10186_CR32","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.isprsjprs.2023.03.007","volume":"198","author":"S Mohammadi","year":"2023","unstructured":"Mohammadi S, Belgiu M, Stein A (2023) Improvement in crop mapping from satellite image time series by effectively supervising deep neural networks. ISPRS J Photogrammetry Remote Sens  198:272-283","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"key":"10186_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11063-022-10783-z","volume":"54","author":"W Jiang","year":"2022","unstructured":"Jiang W, Zhang D, Ling L, Lin R (2022) Time Series Classification Based on Image Transformation Using Feature Fusion Strategy. Neural Process Lett 54:1-22","journal-title":"Neural Process Lett"},{"key":"10186_CR34","doi-asserted-by":"publisher","first-page":"3453","DOI":"10.3390\/s23073453","volume":"23","author":"H Lee","year":"2023","unstructured":"Lee H, Lee J (2023) Convolutional model with a time series feature based on RSSI analysis with the markov transition field for enhancement of location recognition. Sensors 23:3453","journal-title":"Sensors"},{"key":"10186_CR35","doi-asserted-by":"crossref","unstructured":"Dias D, Dias U, Menini N, Lamparelli R, le Maire G, Torres R (2019) Pixelwise remote sensing image classification based on recurrence plot deep features. 1310-1313","DOI":"10.1109\/IGARSS.2019.8898128"},{"issue":"5","key":"10186_CR36","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1007\/s42979-021-00735-0","volume":"2","author":"A Mumuni","year":"2021","unstructured":"Mumuni A, Mumuni F (2021) Cnn architectures for geometric transformation-invariant feature representation in computer vision: a review. SN Comput Sci 2(5):340","journal-title":"SN Comput Sci"},{"key":"10186_CR37","doi-asserted-by":"crossref","unstructured":"Gbodjo Y, Ienco D, Leroux L, Interdonato R, Gaetano R, Ndao B, Dupuy S (2019) Object-based multi-temporal and multi-source land cover mapping leveraging hierarchical class relationships","DOI":"10.3390\/rs12172814"},{"key":"10186_CR38","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.isprsjprs.2019.01.011","volume":"149","author":"R Interdonato","year":"2019","unstructured":"Interdonato R, Ienco D, Gaetano R, Ose K (2019) DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn. ISPRS J Photogrammetry Remote Sens 149:91-104","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"key":"10186_CR39","doi-asserted-by":"publisher","first-page":"2668","DOI":"10.3390\/rs70302668","volume":"7","author":"O Hagolle","year":"2015","unstructured":"Hagolle, O, Huc, M, Pascual, D, Dedieu, Ge (2015) A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENS and Sentinel-2 images. Remote Sens 7:2668-2691. https:\/\/doi.org\/10.3390\/rs70302668","journal-title":"Remote Sens"},{"key":"10186_CR40","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2872132","author":"N Menini","year":"2018","unstructured":"Menini N, Almeida A, Lamparelli R, le Maire G, dos Santos J, Pedrini H, Hirota M, Torres R (2018) A soft computing framework for image classification based on recurrence plots. IEEE Geosci  Remote Sensing Lett. https:\/\/doi.org\/10.1109\/LGRS.2018.2872132","journal-title":"IEEE Geosci Remote Sensing Lett"},{"key":"10186_CR41","doi-asserted-by":"crossref","unstructured":"Achanta R, Shaji A, Smith K, Lucchi A, Fua P, S\u00fasstrunk S (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11)","DOI":"10.1109\/TPAMI.2012.120"},{"key":"10186_CR42","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"10186_CR43","doi-asserted-by":"crossref","unstructured":"Baranwal A, Bagwe BR, Vanitha M (2019) Machine learning in python. Handbook of Research on Applications and Implementations of Machine Learning Techniques 12","DOI":"10.4018\/978-1-5225-9902-9.ch008"},{"key":"10186_CR44","doi-asserted-by":"crossref","unstructured":"Kalita I, Roy M (2022) Inception time dcnn for land cover classification by analyzing multi-temporal remotely sensed images. In: IGARSS 2022 - 2022 IEEE international geoscience and remote sensing symposium","DOI":"10.1109\/IGARSS46834.2022.9884128"},{"key":"10186_CR45","unstructured":"Fawaz HI, Lucas B, Forestier G, Pelletier C, Schmidt, DF, Weber J, Webb GI, Idoumghar L, Muller P-, Petitjean F (2020) Inceptiontime: finding alexnet for time series classification."},{"key":"10186_CR46","unstructured":"Chang WT, Dempster A, Bergmeir C, Webb G (2022) MultiRocket: multiple pooling operators and transformations for fast and effective time series classification. Data Min Knowl Discov"},{"key":"10186_CR47","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1007\/s10618-023-00948-2","volume":"38","author":"NM Foumani","year":"2024","unstructured":"Foumani NM, Tan CW, Webb GI, Salehi M (2024) Improving position encoding of transformers for multivariate time series classification. Data Min Knowl Disc 38:22\u201348","journal-title":"Data Min Knowl Disc"},{"key":"10186_CR48","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30"},{"issue":"29","key":"10186_CR49","doi-asserted-by":"publisher","first-page":"861","DOI":"10.21105\/joss.00861","volume":"3","author":"M Leland","year":"2018","unstructured":"Leland M, Healy NJ, Saul, Lukas G (2018) UMAP: uniform manifold approximation and projection. J Open Sour Softw 3(29):861","journal-title":"J Open Sour Softw"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10186-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10186-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10186-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T07:13:14Z","timestamp":1722669194000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10186-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,3]]},"references-count":49,"alternative-id":["10186"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10186-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,8,3]]},"assertion":[{"value":"31 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}