Abstract
Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.
Highlights
Hyperspectral images (HSIs) are usually acquired in hundreds of narrow contiguous spectral bands by a specific kind of imaging sensor, e.g., the Airborne Visible/Infrared Imaging Spectrometer, Hyperspectral Digital Imagery Collection Experiment and Compact Airborne Spectrographic Imager [1,2,3]
To overcome the above mentioned problems, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM)
The unmixing method based on the BGBM can obtain better reconstruction error (RE) and spectral mean angle distance (SMAD) than these unmixing methods based on the linear mixing model (LMM) and Generalized bilinear model (GBM), which indicate that the BGBM is more suited for the selected real Cuprite HSI than the LMM and GBM
Summary
Hyperspectral images (HSIs) are usually acquired in hundreds of narrow contiguous spectral bands by a specific kind of imaging sensor, e.g., the Airborne Visible/Infrared Imaging Spectrometer, Hyperspectral Digital Imagery Collection Experiment and Compact Airborne Spectrographic Imager [1,2,3]. The second category includes some physical based models, such as intimate mixture model [25], bilinear mixture model (BMM) [26,27,28,29,30,31,32,33] and multilinear mixing model [34,35,36]. Different methods have been proposed for GBM unmixing of HSI, Halimi et al developed a Bayesian algorithm to estimate the abundance and noise variance of the GBM [28]. They proposed a pixel-wise unmixing method based on the gradient descent algorithm (GDA) [29]. Yokoya et al proposed the semi-nonnegative matrix factorization (semi-NMF) as a new optimization method for GBM based
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