{"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/deep-learning-for-target-classification-from","title":"Deep Learning for Target Classification from SAR Imagery: Data Augmentation and Translation Invariance","arxiv_id":"1708.07920","date":"2017-08-26","proceeding":null,"authors":["Hidetoshi Furukawa"],"abstract":"This report deals with translation invariance of convolutional neural\nnetworks (CNNs) for automatic target recognition (ATR) from synthetic aperture\nradar (SAR) imagery. In particular, the translation invariance of CNNs for SAR\nATR represents the robustness against misalignment of target chips extracted\nfrom SAR images. To understand the translation invariance of the CNNs, we\ntrained CNNs which classify the target chips from the MSTAR into the ten\nclasses under the condition of with and without data augmentation, and then\nvisualized the translation invariance of the CNNs. According to our results,\neven if we use a deep residual network, the translation invariance of the CNN\nwithout data augmentation using the aligned images such as the MSTAR target\nchips is not so large. A more important factor of translation invariance is the\nuse of augmented training data. Furthermore, our CNN using augmented training\ndata achieved a state-of-the-art classification accuracy of 99.6%. These\nresults show an importance of domain-specific data augmentation.","url_abs":"http://arxiv.org/abs/1708.07920v1","url_pdf":"http://arxiv.org/pdf/1708.07920v1.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":"deep-learning-for-target-classification-from","repo_url":"https://github.com/fudanxu/MSTAR-AConvNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"deep-learning-for-target-classification-from","repo_url":"https://github.com/singh-shakti94/Deep-Learning-Project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-learning-for-target-classification-from","repo_url":"https://github.com/jangsoopark/AConvNet-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}