{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:14:22Z","timestamp":1777130062245,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T00:00:00Z","timestamp":1620172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Big data analysis assumes a significant role in Earth observation using remote sensing images, since the explosion of data images from multiple sensors is used in several fields. The traditional data analysis techniques have different limitations on storing and processing massive volumes of data. Besides, big remote sensing data analytics demand sophisticated algorithms based on specific techniques to store to process the data in real-time or in near real-time with high accuracy, efficiency, and high speed. In this paper, we present a method for storing a huge number of heterogeneous satellite images based on Hadoop distributed file system (HDFS) and Apache Spark. We also present how deep learning algorithms such as VGGNet and UNet can be beneficial to big remote sensing data processing for feature extraction and classification. The obtained results prove that our approach outperforms other methods.<\/jats:p>","DOI":"10.3390\/bdcc5020021","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T11:06:01Z","timestamp":1620212761000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Big Remote Sensing Image Classification Based on Deep Learning Extraction Features and Distributed Spark Frameworks"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8340-8201","authenticated-orcid":false,"given":"Imen","family":"Chebbi","sequence":"first","affiliation":[{"name":"LIASD Laboratory, University Paris 8, 93200 Paris, France"},{"name":"RIADI Laboratory, University Manouba, Manouba 2010, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8858-9902","authenticated-orcid":false,"given":"Nedra","family":"Mellouli","sequence":"additional","affiliation":[{"name":"LIASD Laboratory, University Paris 8, 93200 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9114-5659","authenticated-orcid":false,"given":"Imed Riadh","family":"Farah","sequence":"additional","affiliation":[{"name":"RIADI Laboratory, University Manouba, Manouba 2010, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myriam","family":"Lamolle","sequence":"additional","affiliation":[{"name":"LIASD Laboratory, University Paris 8, 93200 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,5]]},"reference":[{"key":"ref_1","unstructured":"Chebbi, I., Boulila, W., and Farah, I.R. (2015, January 21\u201323). Big Data: Concepts, Challenges and Applications. Proceedings of the 7th International Conference, ICCCI 2015, Madrid, Spain."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Patgiri, R., and Ahmed, A. (2016, January 12\u201314). Big Data: The V\u2019s of the Game Changer Paradigm. Proceedings of the 18th IEEE High Performance Computing and Communications, Sydney, NSW, Australia.","DOI":"10.1109\/HPCC-SmartCity-DSS.2016.0014"},{"key":"ref_3","unstructured":"(2021, February 04). Apache Hadoop. Available online: https:\/\/hadoop.apache.org\/."},{"key":"ref_4","unstructured":"(2021, February 04). MapReduce. Available online: https:\/\/hadoop.apache.org\/docs\/current\/hadoop-mapreduce-client\/hadoop-mapreduce-client-core\/MapReduceTutorial.html."},{"key":"ref_5","unstructured":"(2021, February 04). Apache Spark. Available online: https:\/\/spark.apache.org\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MS.2016.35","article-title":"Building Pipelines for Heterogeneous Execution Environments for Big Data Processing","volume":"33","author":"Wu","year":"2016","journal-title":"IEEE Softw."},{"key":"ref_7","first-page":"1915","article-title":"Efficient storage method for massive remote sensing image via spark-based pyramid model","volume":"13","author":"Yang","year":"2017","journal-title":"Int. J. Innov. Comput. Inf. Control"},{"key":"ref_8","unstructured":"(2021, February 04). HDFS. Available online: https:\/\/hadoop.apache.org\/docs\/r1.2.1\/hdfsdesign.html."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4340","DOI":"10.1109\/TGRS.2020.3016820","article-title":"More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification","volume":"59","author":"Hong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1109\/ACCESS.2014.2325029","article-title":"Big Data Deep Learning: Challenges and Perspectives","volume":"2","author":"Chen","year":"2014","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","unstructured":"Mamavi, O. (2021, January 31). Note de Lecture: Tensorflow et Keras\u2014L\u2019intelligence Artificielle Appliqu\u00e9e. Available online: https:\/\/management-datascience.org\/articles\/13962\/."},{"key":"ref_13","unstructured":"Goldsborough, P. (2016). A Tour of TensorFlow. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gupta, A., Thakur, H.K., Shrivastava, R., Kumar, P., and Nag, S. (2017, January 18\u201321). A Big Data Analysis Framework Using Apache Spark and Deep Learning. Proceedings of the 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, USA.","DOI":"10.1109\/ICDMW.2017.9"},{"key":"ref_15","first-page":"1235","article-title":"MLlib: Machine Learning in Apache Spark","volume":"17","author":"Meng","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"114417","DOI":"10.1016\/j.eswa.2020.114417","article-title":"A review of deep learning methods for semantic segmentation of remote sensing imagery","volume":"169","author":"Yuan","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_17","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_18","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 Sensing Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2237","DOI":"10.1063\/1.4825984","article-title":"Satellite image classification using convolutional learning","volume":"Volume 1558","author":"Nguyen","year":"2013","journal-title":"AIP Conference Proceedings"},{"key":"ref_20","unstructured":"Castelluccio, M., Poggi, G., Sansone, C., and Verdoliva, L. (2015). Land use classification in remote sensing images by convolutional neural networks. arXiv."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/LGRS.2015.2478256","article-title":"High-Resolution SAR Image Classification via Deep Convolutional Autoencoders","volume":"12","author":"Geng","year":"2015","journal-title":"IEEE Geosci. Remote Sensing Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/JSTARS.2016.2547020","article-title":"In-Memory Parallel Processing of Massive Remotely Sensed Data Using an Apache Spark on Hadoop YARN Model","volume":"10","author":"Huang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tang, S., He, B., Yu, C., Li, Y., and Li, K. (2020). A Survey on Spark Ecosystem: Big Data Processing Infrastructure, Machine Learning, and Applications. IEEE Trans. Knowl. Data Eng.","DOI":"10.1109\/TKDE.2020.2975652"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1007\/s11227-020-03328-5","article-title":"Investigating the performance of Hadoop and Spark platforms on machine learning algorithms","volume":"77","author":"Mostafaeipour","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_26","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., and Davis, A. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yao, Y., Liang, H., Li, X., Zhang, J., and He, J. (2017). Sensing Urban Land-Use Patterns By Integrating Google Tensorflow And Scene-Classification Models. arXiv.","DOI":"10.5194\/isprs-archives-XLII-2-W7-981-2017"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s40537-019-0254-8","article-title":"An analytical study of information extraction from unstructured and multidimensional big data","volume":"6","author":"Adnan","year":"2019","journal-title":"J. Big Data"},{"key":"ref_29","first-page":"151","article-title":"Using Inclusive Language in the Applied-Science Academic Environments","volume":"9","author":"Taheri","year":"2020","journal-title":"Technium Soc. Sci. J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chebbi, I., Mellouli, N., Lamolle, M., and Farah, I.R. (2019, January 17\u201319). Deep Learning Analysis for Big Remote Sensing Image Classification. Proceedings of the KDIR 2019, 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Vienna, Austria.","DOI":"10.5220\/0008166303550362"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Balti, H., Mellouli, N., Chebbi, I., Farah, I.R., and Lamolle, M. (2019, January 17\u201319). Deep Semantic Feature Detection from Multispectral Satellite Images. Proceedings of the KDIR 2019, 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Vienna, Austria.","DOI":"10.5220\/0008350004580466"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Balti, H., Chebbi, I., Mellouli, N., Farah, I.R., and Lamolle, M. (2019, January 16\u201318). A big remote sensing data analysis using deep learning framework. Proceedings of the International Conference Big Data Analytics, Data Mining and Computational Intelligence, Porto, Portugal.","DOI":"10.33965\/bigdaci2019_201907L015"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.1109\/TGRS.2015.2496185","article-title":"Dirichlet-derived multiple topic scene clas-sification model fusing heterogeneous features for high spatial resolution remotesensing imagery","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Dbag-of-visual-words and spatial extensions forland-use classification. Proceedings of the ACM SIGSPATIAL International Conference on Advancesin Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A benchmark data set for performance evaluation of aerial scene classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/5\/2\/21\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:57:20Z","timestamp":1760162240000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/5\/2\/21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,5]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["bdcc5020021"],"URL":"https:\/\/doi.org\/10.3390\/bdcc5020021","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,5]]}}}