Imaging Components, Systems, and Processing

Reconstruction of hyperspectral image using matting model for classification

[+] Author Affiliations
Weiying Xie, Yunsong Li, Chiru Ge

Xidian University, State Key Laboratory of Integrated Service Network, Xi’an 710071, China

Xidian University, Joint Laboratory of High Speed Multi-Source Image Coding and Processing, Xi’an 710071, China

Opt. Eng. 55(5), 053104 (May 09, 2016). doi:10.1117/1.OE.55.5.053104
History: Received September 21, 2015; Accepted April 8, 2016
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Abstract.  Although hyperspectral images (HSIs) captured by satellites provide much information in spectral regions, some bands are redundant or have large amounts of noise, which are not suitable for image analysis. To address this problem, we introduce a method for reconstructing the HSI with noise reduction and contrast enhancement using a matting model for the first time. The matting model refers to each spectral band of an HSI that can be decomposed into three components, i.e., alpha channel, spectral foreground, and spectral background. First, one spectral band of an HSI with more refined information than most other bands is selected, and is referred to as an alpha channel of the HSI to estimate the hyperspectral foreground and hyperspectral background. Finally, a combination operation is applied to reconstruct the HSI. In addition, the support vector machine (SVM) classifier and three sparsity-based classifiers, i.e., orthogonal matching pursuit (OMP), simultaneous OMP, and OMP based on first-order neighborhood system weighted classifiers, are utilized on the reconstructed HSI and the original HSI to verify the effectiveness of the proposed method. Specifically, using the reconstructed HSI, the average accuracy of the SVM classifier can be improved by as much as 19%.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Weiying Xie ; Yunsong Li and Chiru Ge
"Reconstruction of hyperspectral image using matting model for classification", Opt. Eng. 55(5), 053104 (May 09, 2016). ; http://dx.doi.org/10.1117/1.OE.55.5.053104


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