Hyperspectral super-resolution of locally low rank images from complementary multisource data

Abstract : Remote sensing hyperspectral images (HSI) are quite often low rank, in the sense that the data belong to a low dimensional subspace/manifold. This has been recently exploited for the fusion of low spatial resolution HSI with high spatial resolution multispectral images (MSI) in order to obtain super-resolution HSI. Most approaches adopt an unmixing or a matrix factorization perspective. The derived methods have led to state-of-the-art results when the spectral information lies in a low dimensional subspace/manifold. However, if the subspace/manifold dimensionality spanned by the complete data set is large, i.e., larger than the number of multispectral bands, the performance of these methods decrease mainly because the underlying sparse regression problem is severely ill-posed. In this paper, we propose a local approach to cope with this difficulty. Fundamentally, we exploit the fact that real world HSI are locally low rank, that is, pixels acquired from a given spatial neighborhood span a very low dimensional subspace/manifold, i.e., lower or equal than the number of multispectral bands. Thus, we propose to partition the image into patches and solve the data fusion problem independently for each patch. This way, in each patch the subspace/manifold dimensionality is low enough such that the problem is not ill-posed anymore. We propose two alternative approaches to define the hyperspectral super-resolution via local dictionary learning using endmember induction algorithms (HSR-LDL-EIA). We also explore two alternatives to define the local regions, using sliding windows and binary partition trees. The effectiveness of the proposed approaches is illustrated with synthetic and semi real data.
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IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2015, 〈10.1109/TIP.2015.2496263〉
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Contributeur : Miguel Angel Veganzones <>
Soumis le : jeudi 12 novembre 2015 - 12:00:09
Dernière modification le : jeudi 15 novembre 2018 - 20:27:39

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Miguel Veganzones, Miguel Simoes, Giorgio Licciardi, Naoto Yokoya, Jose Bioucas-Dias, et al.. Hyperspectral super-resolution of locally low rank images from complementary multisource data. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2015, 〈10.1109/TIP.2015.2496263〉. 〈hal-01117253v3〉

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