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 conference proceedings. They include the speaker's narration along with a video recording of the presentation slides and animations. Many conference presentations also include full-text papers. Search and browse our growing collection of more than 14,000 conference presentations, including many plenary and keynote presentations.