The Eigen-Template (ET) based closed-set feature extraction approach is extended to an open-set HRR-ATR framework to develop an Open Set Probabilistic Support Vector Machine (OSP-SVM) classifier. The proposed ET-OSP-SVM is shown to perform open set ATR on HRR data with 80% PCC for a 4-class MSTAR dataset.
Jason D. Roos and Arnab K. Shaw, "Probabilistic SVM for open set automatic target recognition on high range resolution radar data," Proc. SPIE 10202, Automatic Target Recognition XXVII, 102020B (Presented at SPIE Defense + Security: April 10, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2262840.
Conference Presentations are recordings of oral presentations given at SPIE conferences and published as part of the proceedings. They include the speaker's narration with video of the slides and animations. Most include full-text papers. Interactive, searchable transcripts and closed captioning are now available for 2018 presentations, with transcripts for prior recordings added daily.
Search our growing collection of more than 16,000 conference presentations, including many plenaries and keynotes.