{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T22:28:33Z","timestamp":1759616913929,"version":"3.37.3"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62027827"],"award-info":[{"award-number":["62027827"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of King Saud University - Computer and Information Sciences"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1016\/j.jksuci.2022.10.029","type":"journal-article","created":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T16:29:44Z","timestamp":1667320184000},"page":"10405-10422","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"PB","title":["Classification from Sky: A Robust Remote Sensing Time Series Image Classification Using Spatial Encoder and Multi-Fast Channel Attention"],"prefix":"10.1007","volume":"34","author":[{"given":"Kwabena","family":"Sarpong","sequence":"first","affiliation":[]},{"given":"Jehoiada Kofi","family":"Jackson","sequence":"additional","affiliation":[]},{"given":"Derrick","family":"Effah","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Addo","sequence":"additional","affiliation":[]},{"given":"Sophyani Banaamwini","family":"Yussif","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Awrangjeb","sequence":"additional","affiliation":[]},{"given":"Rutherford Agbeshi","family":"Patamia","sequence":"additional","affiliation":[]},{"given":"Juliana Mantebea","family":"Danso","sequence":"additional","affiliation":[]},{"given":"Zhiguang","family":"Qin","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"key":"10.1016\/j.jksuci.2022.10.029_b0005","unstructured":"Ba, J., Kiros, J.R., Hinton, G.E., 2016. Layer normalization. ArXiv, abs\/1607.06450."},{"key":"10.1016\/j.jksuci.2022.10.029_b0010","doi-asserted-by":"crossref","unstructured":"Bailly, S., Giordano, S., Landrieu, L., Chehata, N., 2018. Crop-rotation structured classification using multi-source sentinel images and lpis for crop type mapping. In: IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, pp. 1950\u20131953.","DOI":"10.1109\/IGARSS.2018.8518427"},{"key":"10.1016\/j.jksuci.2022.10.029_b0015","doi-asserted-by":"crossref","unstructured":"Christophe, E., Inglada, J., Giros, A., 2008. Orfeo toolbox: A complete solution for mapping from high resolution satellite images.","DOI":"10.1109\/ICIP.2007.4379859"},{"key":"10.1016\/j.jksuci.2022.10.029_b0020","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y., 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555."},{"key":"10.1016\/j.jksuci.2022.10.029_b0025","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: Esa\u2019s optical high-resolution mission for gmes operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"issue":"20","key":"10.1016\/j.jksuci.2022.10.029_b0030","doi-asserted-by":"crossref","first-page":"4113","DOI":"10.1080\/01431160410001698870","article-title":"Crop yield estimation by satellite remote sensing","volume":"25","author":"Ferencz","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.jksuci.2022.10.029_b0035","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.compag.2012.07.015","article-title":"Crop type mapping using spectral\u2013temporal profiles and phenological information","volume":"89","author":"Foerster","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.jksuci.2022.10.029_b0040","series-title":"International Workshop on Advanced Analytics and Learning on Temporal Data","first-page":"171","article-title":"Lightweight temporal self-attention for classifying satellite images time series","author":"Garnot","year":"2020"},{"key":"10.1016\/j.jksuci.2022.10.029_b0045","unstructured":"Garnot, V.S.F., Landrieu, L., 2021. Panoptic segmentation of satellite image time series with convolutional temporal attention networks. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4852\u20134861."},{"key":"10.1016\/j.jksuci.2022.10.029_b0050","doi-asserted-by":"crossref","unstructured":"Garnot, V.S.F., Landrieu, L., Giordano, S., Chehata, N., 2019. Time-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series. In: IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, pp. 6247\u20136250.","DOI":"10.1109\/IGARSS.2019.8900517"},{"key":"10.1016\/j.jksuci.2022.10.029_b0055","unstructured":"Garnot, V.S.F., Landrieu, L., Giordano, S., Chehata, N., 2020. Satellite image time series classification with pixel-set encoders and temporal self-attention. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12322\u201312331."},{"key":"10.1016\/j.jksuci.2022.10.029_b0060","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F., Brendel, W., 2019. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. ArXiv, abs\/1811.12231."},{"key":"10.1016\/j.jksuci.2022.10.029_b0065","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-excitation networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"10.1016\/j.jksuci.2022.10.029_b0070","doi-asserted-by":"crossref","first-page":"12356","DOI":"10.3390\/rs70912356","article-title":"Assessment of an operational system for crop type map production using high temporal and spatial resolution satellite optical imagery","volume":"7","author":"Inglada","year":"2015","journal-title":"Remote Sensing"},{"key":"10.1016\/j.jksuci.2022.10.029_b0075","doi-asserted-by":"crossref","unstructured":"Interdonato, R., Ienco, D., Gaetano, R., Ose, K., 2019. Duplo: A dual view point deep learning architecture for time series classification. ArXiv, abs\/1809.07589.","DOI":"10.1016\/j.isprsjprs.2019.01.011"},{"key":"10.1016\/j.jksuci.2022.10.029_b0080","unstructured":"Ioffe, S., Szegedy, C., 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. ArXiv, abs\/1502.03167."},{"issue":"1","key":"10.1016\/j.jksuci.2022.10.029_b0085","doi-asserted-by":"crossref","first-page":"75","DOI":"10.3390\/rs10010075","article-title":"3d convolutional neural networks for crop classification with multi-temporal remote sensing images","volume":"10","author":"Ji","year":"2018","journal-title":"Remote Sens."},{"key":"10.1016\/j.jksuci.2022.10.029_b0090","doi-asserted-by":"crossref","unstructured":"Kim, Y., 2014. Convolutional neural networks for sentence classification. In: EMNLP.","DOI":"10.3115\/v1\/D14-1181"},{"key":"10.1016\/j.jksuci.2022.10.029_b0095","unstructured":"Kingma, D.P., Ba, J., 2015. Adam: A method for stochastic optimization. CoRR, abs\/1412.6980."},{"issue":"5","key":"10.1016\/j.jksuci.2022.10.029_b0100","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.jksuci.2022.10.029_b0105","doi-asserted-by":"crossref","unstructured":"Kussul, N., Lemoine, G., Gallego, J., Skakun, S., Lavreniuk, M., 2015. Parcel based classification for agricultural mapping and monitoring using multi-temporal satellite image sequences. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 165\u2013168.","DOI":"10.1109\/IGARSS.2015.7325725"},{"key":"10.1016\/j.jksuci.2022.10.029_b0110","doi-asserted-by":"crossref","unstructured":"Lee, H., Kim, H.-E., Nam, H., 2019. Srm: A style-based recalibration module for convolutional neural networks. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1854\u20131862.","DOI":"10.1109\/ICCV.2019.00194"},{"key":"10.1016\/j.jksuci.2022.10.029_b0115","doi-asserted-by":"crossref","first-page":"6438","DOI":"10.1109\/JSTARS.2021.3090418","article-title":"Semantic segmentation of remote sensing images with self-supervised multitask representation learning","volume":"14","author":"Li","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obser. Remote Sens."},{"key":"10.1016\/j.jksuci.2022.10.029_b0120","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P., 2017. Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"10.1016\/j.jksuci.2022.10.029_b0125","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal loss for dense object detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.jksuci.2022.10.029_b0130","unstructured":"Lipton, Z.C., 2015. A critical review of recurrent neural networks for sequence learning. ArXiv, abs\/1506.00019."},{"issue":"23","key":"10.1016\/j.jksuci.2022.10.029_b0135","doi-asserted-by":"crossref","first-page":"4871","DOI":"10.1080\/0143116031000070490","article-title":"Classification of wheat crop with multi-temporal images: Performance of maximum likelihood and artificial neural networks","volume":"24","author":"Murthy","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.jksuci.2022.10.029_b0140","unstructured":"Nair, V., Hinton, G.E., 2010. Rectified linear units improve restricted boltzmann machines. In: ICML."},{"issue":"8","key":"10.1016\/j.jksuci.2022.10.029_b0145","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.3390\/rs10081217","article-title":"Deep recurrent neural network for agricultural classification using multitemporal sar sentinel-1 for camargue, France","volume":"10","author":"Ndikumana","year":"2018","journal-title":"Remote Sensing"},{"key":"10.1016\/j.jksuci.2022.10.029_b0150","series-title":"2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","first-page":"192","article-title":"A deep learning hybrid cnn framework approach for vegetation cover mapping using deep features","author":"Nijhawan","year":"2017"},{"key":"10.1016\/j.jksuci.2022.10.029_b0155","doi-asserted-by":"crossref","unstructured":"Nyborg, J., Pelletier, C., Assent, I., 2022. Generalized classification of satellite image time series with thermal positional encoding. ArXiv, abs\/2203.09175.","DOI":"10.1109\/CVPRW56347.2022.00145"},{"key":"10.1016\/j.jksuci.2022.10.029_b0160","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Machine Learn. Res."},{"key":"10.1016\/j.jksuci.2022.10.029_b0165","doi-asserted-by":"crossref","first-page":"523","DOI":"10.3390\/rs11050523","article-title":"Temporal convolutional neural network for the classification of satellite image time series","volume":"11","author":"Pelletier","year":"2019","journal-title":"Remote. Sens."},{"key":"10.1016\/j.jksuci.2022.10.029_b0170","unstructured":"Ru\u00dfwurm, M., K\u00f6rner, M., 2018a. Convolutional lstms for cloud-robust segmentation of remote sensing imagery. ArXiv, abs\/1811.02471."},{"key":"10.1016\/j.jksuci.2022.10.029_b0175","unstructured":"Ru\u00dfwurm, M., K\u00f6rner, M., 2018b. Convolutional lstms for cloud-robust segmentation of remote sensing imagery. arXiv preprint arXiv:1811.02471."},{"key":"10.1016\/j.jksuci.2022.10.029_b0180","unstructured":"Ru\u00dfwurm, M., K\u00f6rner, M., 2019. Self-attention for raw optical satellite time series classification. ArXiv, abs\/1910.10536."},{"key":"10.1016\/j.jksuci.2022.10.029_b0185","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., K\u00f6rner, M., 2017. Temporal vegetation modelling using long short-term memory networks for crop identification from medium-resolution multi-spectral satellite images. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1496\u20131504.","DOI":"10.1109\/CVPRW.2017.193"},{"key":"10.1016\/j.jksuci.2022.10.029_b0190","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.rse.2012.10.010","article-title":"Long-term land cover dynamics by multi-temporal classification across the landsat-5 record","volume":"128","author":"Sexton","year":"2013","journal-title":"Remote Sens. Environ."},{"issue":"4","key":"10.1016\/j.jksuci.2022.10.029_b0195","doi-asserted-by":"crossref","first-page":"3633","DOI":"10.3390\/rs70403633","article-title":"A hidden markov models approach for crop classification: Linking crop phenology to time series of multi-sensor remote sensing data","volume":"7","author":"Siachalou","year":"2015","journal-title":"Remote Sensing"},{"key":"10.1016\/j.jksuci.2022.10.029_b0200","first-page":"1","article-title":"Channel attention-based temporal convolutional network for satellite image time series classification","volume":"19","author":"Tang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.jksuci.2022.10.029_b0205","series-title":"Multispectral Satellite Image Understanding","first-page":"49","article-title":"Review on land use classification","author":"\u00dcnsalan","year":"2011"},{"key":"10.1016\/j.jksuci.2022.10.029_b0210","unstructured":"Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I., 2017. Attention is all you need. In: NIPS."},{"key":"10.1016\/j.jksuci.2022.10.029_b0215","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.rse.2018.03.014","article-title":"Vegetation phenology from sentinel-2 and field cameras for a dutch barrier island","volume":"215","author":"Vrieling","year":"2018","journal-title":"Remote Sensing Environ."},{"key":"10.1016\/j.jksuci.2022.10.029_b0220","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.jag.2018.06.007","article-title":"How much does multi-temporal sentinel-2 data improve crop type classification?","volume":"72","author":"Vuolo","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinformat."},{"key":"10.1016\/j.jksuci.2022.10.029_b0225","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q., 2020. Eca-net: Efficient channel attention for deep convolutional neural networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11531\u201311539.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"10.1016\/j.jksuci.2022.10.029_b0230","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.rse.2007.07.019","article-title":"Large-area crop mapping using time-series modis 250 m ndvi data: An assessment for the u.s. central great plains","volume":"112","author":"Wardlow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.jksuci.2022.10.029_b0235","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.-S., 2018. Cbam: Convolutional block attention module. In: ECCV.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"10.1016\/j.jksuci.2022.10.029_b0240","doi-asserted-by":"crossref","unstructured":"Wu, Y., He, K., 2018. Group normalization. In: ECCV.","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"10.1016\/j.jksuci.2022.10.029_b0245","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/JSTARS.2020.3036602","article-title":"Self-supervised pretraining of transformers for satellite image time series classification","volume":"14","author":"Yuan","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obser. Remote Sens."},{"issue":"11","key":"10.1016\/j.jksuci.2022.10.029_b0250","doi-asserted-by":"crossref","first-page":"1840","DOI":"10.3390\/rs10111840","article-title":"Mapping paddy rice using a convolutional neural network (cnn) with landsat 8 datasets in the dongting lake area, china","volume":"10","author":"Zhang","year":"2018","journal-title":"Remote Sens."},{"key":"10.1016\/j.jksuci.2022.10.029_b0255","first-page":"1","article-title":"Attention-aware dynamic self-aggregation network for satellite image time series classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.jksuci.2022.10.029_b0260","first-page":"103","article-title":"A support vector machine to identify irrigated crop types using time-series landsat ndvi data","volume":"34","author":"Zheng","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.jksuci.2022.10.029_b0265","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2014.06.012","article-title":"Accurate mapping of forest types using dense seasonal landsat time-series","volume":"96","author":"Zhu","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Journal of King Saud University - Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1319157822003962?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1319157822003962?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T17:22:48Z","timestamp":1736184168000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1319157822003962"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11]]},"references-count":53,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["S1319157822003962"],"URL":"https:\/\/doi.org\/10.1016\/j.jksuci.2022.10.029","relation":{},"ISSN":["1319-1578"],"issn-type":[{"type":"print","value":"1319-1578"}],"subject":[],"published":{"date-parts":[[2022,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Classification from Sky: A Robust Remote Sensing Time Series Image Classification Using Spatial Encoder and Multi-Fast Channel Attention","name":"articletitle","label":"Article Title"},{"value":"Journal of King Saud University - Computer and Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jksuci.2022.10.029","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University.","name":"copyright","label":"Copyright"}]}}