{"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/classifying-multi-channel-uwb-sar-imagery-via","title":"Classifying Multi-channel UWB SAR Imagery via Tensor Sparsity Learning Techniques","arxiv_id":"1810.02812","date":"2018-10-04","proceeding":null,"authors":["Tiep Vu","Lam Nguyen","Vishal Monga"],"abstract":"Using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture\nradar (SAR) technology for detecting buried and obscured targets, e.g. bomb or\nmine, has been successfully demonstrated recently. Despite promising recent\nprogress, a significant open challenge is to distinguish obscured targets from\nother (natural and manmade) clutter sources in the scene. The problem becomes\nexacerbated in the presence of noisy responses from rough ground surfaces. In\nthis paper, we present three novel sparsity-driven techniques, which not only\nexploit the subtle features of raw captured data but also take advantage of the\npolarization diversity and the aspect angle dependence information from\nmulti-channel SAR data. First, the traditional sparse representation-based\nclassification (SRC) is generalized to exploit shared information of classes\nand various sparsity structures of tensor coefficients for multi-channel data.\nCorresponding tensor dictionary learning models are consequently proposed to\nenhance classification accuracy. Lastly, a new tensor sparsity model is\nproposed to model responses from multiple consecutive looks of objects, which\nis a unique characteristic of the dataset we consider. Extensive experimental\nresults on a high-fidelity electromagnetic simulated dataset and radar data\ncollected from the U.S. Army Research Laboratory side-looking SAR demonstrate\nthe advantages of proposed tensor sparsity models.","url_abs":"http://arxiv.org/abs/1810.02812v1","url_pdf":"http://arxiv.org/pdf/1810.02812v1.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":"classifying-multi-channel-uwb-sar-imagery-via","repo_url":"https://github.com/tiepvupsu/tensorsparsity","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"dictionary-learning","task_name":"Dictionary Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sparse-representation-based-classification","task_name":"Sparse Representation-based Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}