A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Spectral unmixing is the process of decomposing the measured spectrum of a mixed pixel into a set of pure spectral signatures called endmembers and their corresponding abundances, which indicate the fractional area coverage of each endmember present in the pixel. A substantial number of spectral unmixing studies rely on a spectral mixture model which assumes that spectral mixing only occurs within the extent of a pixel. However, due to adjacency effect, the spectral measurement of the pixel may be contaminated by radiance from materials in neighboring pixels. In this paper, a linear spatial spectral mixture model that incorporates an adjacency effect in abundance estimation is proposed. We extend the classic linear mixture model by including a spatial term that expresses for each pixel the spectral contributions from its nearby pixels. An iterative optimization algorithm is developed to estimate fractional abundances of endmembers and a coefficient representing the overall intensity of the adjacency effect in the image. Our experimental results, with both synthetic and real hyperspectral images, demonstrate the effectiveness of the proposed model.
Spectral unmixing is the process of decomposing the measured spectrum of a mixed pixel into a set of pure spectral signatures called endmembers and their corresponding abundances, which indicate the fractional area coverage of each endmember present in the pixel. A substantial number of spectral unmixing studies rely on a spectral mixture model which assumes that spectral mixing only occurs within the extent of a pixel. However, due to adjacency effect, the spectral measurement of the pixel may be contaminated by radiance from materials in neighboring pixels. In this paper, a linear spatial spectral mixture model that incorporates an adjacency effect in abundance estimation is proposed. We extend the classic linear mixture model by including a spatial term that expresses for each pixel the spectral contributions from its nearby pixels. An iterative optimization algorithm is developed to estimate fractional abundances of endmembers and a coefficient representing the overall intensity of the adjacency effect in the image. Our experimental results, with both synthetic and real hyperspectral images, demonstrate the effectiveness of the proposed model.
Linear Spatial Spectral Mixture Model
2016
Article (Journal)
English
Local classification TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
Adaptive Linear Spectral Mixture Analysis
| Online Contents | 2017
Classification - Fisher's Linear Spectral Mixture Analysis
| Online Contents | 2006
High spatial resolution spectral mixture analysis of urban reflectance
| Online Contents | 2003