A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Robust Sparse Hyperspectral Unmixing With \ell Norm
Sparse unmixing (SU) of hyperspectral data have recently received particular attention for analyzing remote sensing images, which aims at finding the optimal subset of signatures to best model the mixed pixel in the scene. However, most SU methods are based on the commonly admitted linear mixing model, which ignores the possible nonlinear effects (i.e., nonlinearity), and the nonlinearity is merely treated as outlier. Besides, the traditional SU algorithms often adopt the \ell _{2} norm loss function, which makes them sensitive to noises and outliers. In this paper, we propose a robust SU (RSU) method with \ell _{2,1} norm loss function, which is robust for noises and outliers. Then, the RSU can be solved by the alternative direction method of multipliers. Finally, the experiments on both synthetic data sets and real hyperspectral images demonstrate that the proposed RSU is efficient for solving the hyperspectral SU problem compared with the state-of-the-art algorithms.
Robust Sparse Hyperspectral Unmixing With \ell Norm
Sparse unmixing (SU) of hyperspectral data have recently received particular attention for analyzing remote sensing images, which aims at finding the optimal subset of signatures to best model the mixed pixel in the scene. However, most SU methods are based on the commonly admitted linear mixing model, which ignores the possible nonlinear effects (i.e., nonlinearity), and the nonlinearity is merely treated as outlier. Besides, the traditional SU algorithms often adopt the \ell _{2} norm loss function, which makes them sensitive to noises and outliers. In this paper, we propose a robust SU (RSU) method with \ell _{2,1} norm loss function, which is robust for noises and outliers. Then, the RSU can be solved by the alternative direction method of multipliers. Finally, the experiments on both synthetic data sets and real hyperspectral images demonstrate that the proposed RSU is efficient for solving the hyperspectral SU problem compared with the state-of-the-art algorithms.
Robust Sparse Hyperspectral Unmixing With \ell Norm
Ma, Yong (author) / Li, Chang / Mei, Xiaoguang / Liu, Chengyin / Ma, Jiayi
2017
Article (Journal)
English
Local classification TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
Sparse Unmixing of Hyperspectral Data
| Online Contents | 2011
Sparse Distributed Multitemporal Hyperspectral Unmixing
| Online Contents | 2017
Collaborative sparse regression for hyperspectral unmixing
| Online Contents | 2014
Collaborative Sparse Regression for Hyperspectral Unmixing
| Online Contents | 2014
Structured Sparse Method for Hyperspectral Unmixing
| Online Contents | 2014