Search Results for author:
Found 3 papers, 0 papers with code
Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation
This paper proposes a new hyperspectral unmixing method for nonlinearly mixed hyperspectral data using a semantic representation in a semi-supervised fashion, assuming the availability of a spectral reference library.
Wavelet-Based Semantic Features for Hyperspectral Signature Discrimination
Hyperspectral signature classification is a quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at the pixel level in the scene.
Perfect Recovery Conditions For Non-Negative Sparse Modeling
Sparse modeling has been widely and successfully used in many applications such as computer vision, machine learning, and pattern recognition.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
