{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:50:53Z","timestamp":1773154253438,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41631179"],"award-info":[{"award-number":["41631179"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the National Key Research and Development Program of China","award":["2017YFB0503600"],"award-info":[{"award-number":["2017YFB0503600"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["2019B17114"],"award-info":[{"award-number":["2019B17114"]}]},{"name":"the Opening Foundation of Key Lab of Spatial Data Mining &amp; Information Sharing, Ministry of Education (Fuzhou University)","award":["2019LSDMIS04"],"award-info":[{"award-number":["2019LSDMIS04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture radar (SAR) data. Central to this method is the use of multiple deep convolutional networks (DCNs) to extract spatial features and to use the long short-term memory (LSTM) network to organize spatial features. First, a precise farmland parcel map was delineated from optical images. Second, hundreds of spatial features were retrieved using multiple DCNs from preprocessed SAR images and overlaid onto the parcel map to construct multivariate time-series of crop growth for parcels. Third, LSTM-based network structures for organizing these time-series features were constructed to produce a final parcel-based classification map. The method was applied to a dataset of high-resolution ZY-3 optical images and multi-temporal Sentinel-1A SAR data to classify crop types in the Hunan Province of China. The classification results, showing an improvement of greater than 5.0% in overall accuracy relative to methods without spatial features, demonstrated the effectiveness of the proposed method in extracting and organizing spatial features for improving parcel-based crop classification.<\/jats:p>","DOI":"10.3390\/rs11131619","type":"journal-article","created":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T11:02:37Z","timestamp":1562583757000},"page":"1619","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4880-6439","authenticated-orcid":false,"given":"Ya\u2019nan","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China"}]},{"given":"Jiancheng","family":"Luo","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Li","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China"}]},{"given":"Xiaocheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining &amp; Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350116, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, Y., Huang, Q., Wu, W., Luo, J., Gao, L., Dong, W., Wu, T., and Hu, X. (2017). Geo-Parcel Based Crop Identification by Integrating High Spatial-Temporal Resolution Imagery from Multi-Source Satellite Data. Remote Sens., 9.","DOI":"10.3390\/rs9121298"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/S0034-4257(00)00212-1","article-title":"Rice monitoring and production estimation using multitemporal RADARSAT","volume":"76","author":"Shao","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhou, T., Pan, J., Zhang, P., Wei, S., and Han, T. (2017). Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region. Sensors, 17.","DOI":"10.3390\/s17061210"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2016.11.004","article-title":"Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery","volume":"188","author":"Gao","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.agsy.2019.01.005","article-title":"Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin","volume":"171","author":"Piedelobo","year":"2019","journal-title":"Agric. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1080\/01431161.2011.587844","article-title":"Crop classification using multi-configuration SAR data in the North China Plain","volume":"33","author":"Jia","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","first-page":"252","article-title":"Early season monitoring of corn and soybeans with TerraSAR-X and RADARSAT-2","volume":"28","author":"McNairn","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Park, S., Im, J., Park, S., Yoo, C., Han, H., and Rhee, J. (2018). Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data. Remote Sens., 10.","DOI":"10.3390\/rs10030447"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2138","DOI":"10.1109\/TGRS.2011.2172994","article-title":"Crop Classification by Multitemporal C-and L-Band Single-and Dual-Polarization and Fully Polarimetric SAR","volume":"50","author":"Skriver","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2585","DOI":"10.1080\/01431161.2016.1182663","article-title":"Evaluation of the discrimination capability of full polarimetric SAR data for crop classification","volume":"37","author":"Zeyada","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1109\/TGRS.2005.852768","article-title":"Efficient texture analysis of SAR imagery","volume":"43","author":"Kandaswamy","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Numbisi, F.N., Van Coillie, F., and De Wulf, R. (2019). Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.20944\/preprints201901.0050.v1"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lu, L., Tao, Y., and Di, L. (2018). Object-Based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10111820"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Inglada, J., Vincent, A., Arias, M., and Marais-Sicre, C. (2016). Improved Early Crop Type Identification by Joint Use of High Temporal Resolution SAR and Optical Image Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8050362"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1006\/ciun.1993.1024","article-title":"A review of recent texture segmentation and feature extraction techniques","volume":"57","author":"Reed","year":"1993","journal-title":"CVGIP Image Underst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1080\/01431161.2016.1278314","article-title":"Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales","volume":"38","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lan, Z., and Liu, Y. (2018). Study on Multi-Scale Window Determination for GLCM Texture Description in High-Resolution Remote Sensing Image Geo-Analysis Supported by GIS and Domain Knowledge. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7050175"},{"key":"ref_19","first-page":"1","article-title":"Target Classification Using the Deep Convolutional Networks for SAR Images","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1080\/01431161.2018.1513666","article-title":"Very high resolution Remote sensing image classification with SEEDS-CNN and scale effect analysis for superpixel CNN classification","volume":"40","author":"Lv","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3368","DOI":"10.1080\/2150704X.2015.1062157","article-title":"On combining multiscale deep learning features for the classification of hyperspectral Remote sensing imagery","volume":"36","author":"Zhao","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, Z., Li, X., and Yeh, A.G.-O. (2019). Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11060690"},{"key":"ref_23","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":"ref_24","doi-asserted-by":"crossref","unstructured":"Teimouri, N., Dyrmann, M., and J\u00f8rgensen, R.N. (2019). A Novel Spatio-Temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images. Remote Sens., 11.","DOI":"10.3390\/rs11080990"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2016.01.004","article-title":"Learning multiscale and deep representations for classifying Remotely sensed imagery","volume":"113","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/TGRS.2015.2478379","article-title":"Unsupervised Deep Feature Extraction for Remote Sensing Image Classification","volume":"54","author":"Romero","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.3390\/rs9121330","article-title":"Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification","volume":"9","author":"Liu","year":"2017","journal-title":"Remote Sens."},{"key":"ref_28","unstructured":"(2019, June 30). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Available online: https:\/\/arxiv.org\/abs\/1506.04214."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.rse.2018.11.032","article-title":"Deep learning based multi-temporal crop classification","volume":"221","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., and Korner, M. (2017, January 21\u201326). Temporal vegetation modelling using long short-term memory networks for crop identification from medium-resolution multi-spectral satellite images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.193"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ndikumana, E., Minh, D.H.T., Baghdadi, N., Courault, D., and Hossard, L. (2018). Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sens., 10.","DOI":"10.3390\/rs10081217"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the European Conference on Computer Vision Proceedings of the Computer Vision\u2013ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2014","journal-title":"Int. J. Comput. Vis."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Han, X., Zhong, Y., Cao, L., and Zhang, L. (2017). Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification. Remote Sens., 9.","DOI":"10.3390\/rs9080848"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.patcog.2016.07.001","article-title":"Towards better exploiting convolutional neural networks for Remote sensing scene classification","volume":"61","author":"Nogueira","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","article-title":"Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_37","unstructured":"Castelluccio, M., Poggi, G., Sansone, C., and Verdoliva, L. (2015). Land Use Classification in Remote Sensing Images by Convolutional Neural Network. arXiv, Available online: https:\/\/arxiv.org\/abs\/1508.00092."},{"key":"ref_38","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv, Available online: https:\/\/arxiv.org\/abs\/1409.1556."},{"key":"ref_39","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., 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_41","doi-asserted-by":"crossref","first-page":"3641","DOI":"10.1109\/JSTARS.2017.2693993","article-title":"Adaptive Scale Selection for Multiscale Segmentation of Satellite Images","volume":"10","author":"Zhou","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","unstructured":"Zhang, R., Li, W., and Mo, T. (2018). Review of Deep Learning. arXiv, Available online: https:\/\/arxiv.org\/abs\/1804.01653."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"042609","DOI":"10.1117\/1.JRS.11.042609","article-title":"A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community","volume":"11","author":"Ball","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_45","first-page":"364","article-title":"Convolutional Neural Network With Data Augmentation for SAR Target Recognition","volume":"13","author":"Ding","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC press.","DOI":"10.1201\/9781420055139"},{"key":"ref_47","first-page":"1","article-title":"The truth of the F-measure","volume":"1","author":"Sasaki","year":"2007","journal-title":"Teach Tutor Mater"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1142\/S0218195912600023","article-title":"Computing the discrete Fr\u00e9chet distance with imprecise input","volume":"22","author":"Ahn","year":"2012","journal-title":"Int. J. Comput. Geom. Appl."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1619\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:03:37Z","timestamp":1760187817000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1619"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,8]]},"references-count":48,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11131619"],"URL":"https:\/\/doi.org\/10.3390\/rs11131619","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,8]]}}}