تقرير
Multiple Instance Hyperspectral Target Characterization
| العنوان: | Multiple Instance Hyperspectral Target Characterization |
|---|---|
| المؤلفون: | Zare, Alina, Jiao, Changzhe, Glenn, Taylor |
| سنة النشر: | 2016 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | In this paper, two methods for multiple instance target characterization, MI-SMF and MI-ACE, are presented. MI-SMF and MI-ACE estimate a discriminative target signature from imprecisely-labeled and mixed training data. In many applications, such as sub-pixel target detection in remotely-sensed hyperspectral imagery, accurate pixel-level labels on training data is often unavailable and infeasible to obtain. Furthermore, since sub-pixel targets are smaller in size than the resolution of a single pixel, training data is comprised only of mixed data points (in which target training points are mixtures of responses from both target and non-target classes). Results show improved, consistent performance over existing multiple instance concept learning methods on several hyperspectral sub-pixel target detection problems. Comment: accepted version after revisions based on reviewer comments |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1606.06354 |
| رقم الأكسشن: | edsarx.1606.06354 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |