{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/artificial-generation-of-big-data-for","title":"Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data","arxiv_id":"1711.02010","date":"2017-11-06","proceeding":null,"authors":["Dimitrios Marmanis","Wei Yao","Fathalrahman Adam","Mihai Datcu","Peter Reinartz","Konrad Schindler","Jan Dirk Wegner","Uwe Stilla"],"abstract":"Very High Spatial Resolution (VHSR) large-scale SAR image databases are still\nan unresolved issue in the Remote Sensing field. In this work, we propose such\na dataset and use it to explore patch-based classification in urban and\nperiurban areas, considering 7 distinct semantic classes. In this context, we\ninvestigate the accuracy of large CNN classification models and pre-trained\nnetworks for SAR imaging systems. Furthermore, we propose a Generative\nAdversarial Network (GAN) for SAR image generation and test, whether the\nsynthetic data can actually improve classification accuracy.","url_abs":"http://arxiv.org/abs/1711.02010v1","url_pdf":"http://arxiv.org/pdf/1711.02010v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"artificial-generation-of-big-data-for","repo_url":"https://github.com/deep-unlearn/Big_Data_From_Space_2017","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.02010","atlas_url":"https://app.syntology.ai/?focus=1711.02010","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}