Discriminative Multiple Instance Hyperspectral Target Characterization
نویسندگان
چکیده
منابع مشابه
Multiple Instance Hyperspectral Target Characterization
In this paper, two methods for multiple instance target characterization, MI-SMF and MI-ACE, are presented. MISMF 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...
متن کاملMultiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection
The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented. In many hyperspectral target detection problems, acquiring accurately labeled training data is difficult. Furthermore, each pixel containing target is likely to be a mixture of both target and non-target signatures (i.e., subpixel targets), making extractin...
متن کاملInstance Influence Estimation for Hyperspectral Target Signature Characterization using Extended Functions of Multiple Instances
The Extended Functions of Multiple Instances (eFUMI) algorithm is a generalization of Multiple Instance Learning (MIL). In eFUMI, only bag level (i.e. set level) labels are needed to estimate target signatures from mixed data. The training bags in eFUMI are labeled positive if any data point in a bag contains or represents any proportion of the target signature and are labeled as a negative bag...
متن کاملDISCRIMINATIVE GRAPHICAL MODELS FORSPARSITY - BASED HYPERSPECTRAL TARGET DETECTION Report
The inherent discriminative capability of sparse representations has been exploited recently for hyperspectral target detection. This approach relies on the observation that the spectral signature of a pixel can be represented as a linear combination of a few training spectra drawn from both target and background classes. The sparse representation corresponding to a given test spectrum captures...
متن کاملDISCRIMINATIVE GRAPHICAL MODELS FORSPARSITY - BASED HYPERSPECTRAL TARGET DETECTION Report Title
The inherent discriminative capability of sparse representations has been exploited recently for hyperspectral target detection. This approach relies on the observation that the spectral signature of a pixel can be represented as a linear combination of a few training spectra drawn from both target and background classes. The sparse representation corresponding to a given test spectrum captures...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2018
ISSN: 0162-8828,2160-9292,1939-3539
DOI: 10.1109/tpami.2017.2756632