Hyperspectral image unsupervised classification by robust manifold matrix factorization

Hyperspectral remote sensing image unsupervised classification, which assigns each pixel of the image into a certain land-cover class without any training samples, plays an important role in the hyperspectral image processing but still leaves huge challenges due to the complicated and high-dimension...

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Vydáno v:Information sciences Ročník 485; s. 154 - 169
Hlavní autoři: Zhang, Lefei, Zhang, Liangpei, Du, Bo, You, Jane, Tao, Dacheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.06.2019
Témata:
Data clustering
Dimensionality reduction
Hyperspectral image
Manifold regularization
Matrix factorization
Dimensionality reduction
Matrix factorization
Hyperspectral image
Manifold regularization
Data clustering
ISSN:0020-0255, 1872-6291
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Shrnutí:Hyperspectral remote sensing image unsupervised classification, which assigns each pixel of the image into a certain land-cover class without any training samples, plays an important role in the hyperspectral image processing but still leaves huge challenges due to the complicated and high-dimensional data observation. Although many advanced hyperspectral remote sensing image classification techniques based on supervised and semi-supervised learning had been proposed and confirmed effective in recent years, they require a certain number of high quality training samples to learn a classifier, and thus can’t work in the unsupervised manner. In this work, we propose a hyperspectral image unsupervised classification framework based on robust manifold matrix factorization and its out-of-sample extension. In order to address the high feature dimensionality of the hyperspectral image, we propose a unified low-rank matrix factorization to jointly perform the dimensionality reduction and data clustering, by which the clustering result can be exactly reproduced, which is significantly superior to the existing data clustering algorithms such as the k-means and spectral clustering. In particular, in the proposed matrix factorization, the ℓ2,1-norm is used to measure the reconstruction loss, which helps to reduce the errors brought by the possible noisy observation. The widely considered manifold regularization is also adopted to further promote the proposed model. Furthermore, we have designed a novel Augmented Lagrangian Method (ALM) based procedure to seek the local optimal solution of the proposed optimization and suggested an additional out-of-sample extension trick to make the method can deal with the large-scale hyperspectral remote sensing images. Several experimental results on the standard hyperspectral images show that the proposed method presents competitive clustering accuracy and comparative running time compared to the existing data clustering algorithms.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2019.02.008