Abstract
Existing hyperspectral sensors usually produce high-spectral-resolution but low-spatial-resolution images, and super-resolution has yielded impressive results in improving the resolution of the hyperspectral images (HSIs). However, most of the super-resolution methods require multiple observations of the same scene and improve the spatial resolution without fully considering the spectral information. In this paper, we propose an HSI super-resolution method inspired by the deep Laplacian pyramid network (LPN). First, the spatial resolution is enhanced by an LPN, which can exploit the knowledge from natural images without using any auxiliary observations. The LPN progressively reconstructs the high-spatial-resolution images in a coarse-to-fine fashion by using multiple pyramid levels. Second, spectral characteristics between the low- and high-resolution HSIs are studied by the non-negative dictionary learning (NDL), which is proposed to learn the common dictionary with non-negative constraints. The super-resolution results can finally be obtained by multiplying the learned dictionary and its corresponding sparse codes. Experimental results on three hyperspectral datasets demonstrate the feasibility of the proposed method in enhancing the spatial resolution of the HSI with preserving the spectral information simultaneously.
Highlights
Hyperspectral imaging systems sample the electromagnetic spectrum of scene radiance by hundreds of contiguous spectral bands
We propose a single-image super-resolution framework inspired by a deep Laplacian pyramid network (LPN) [40,41] to enhance the spatial resolution of the hyperspectral images (HSIs) with the spectral information preserved
It can first be seen that the bicubic, super-resolution CNN (SRCNN) and LPN-negative dictionary learning (NDL) methods yield superior performance compared with other single image methods, while coupled non-negative matrix factorization (CNMF) achieves better or comparable results compared with other auxiliary-based methods
Summary
Hyperspectral imaging systems sample the electromagnetic spectrum of scene radiance by hundreds of contiguous spectral bands. The obtained hyperspectral image (HSI) [1,2,3,4], which has a high spectral resolution, is a data cube containing very narrow spectral bands ranging from the visible to infrared spectrum, enabling the fine representation of different land-covers by spectral signatures. The high spectral resolution comes at a cost, i.e., low-spatial-resolution, that is, the acquired real HSI data usually provides coarse spatial information, and are incapable of capturing the details of different objects. The low-spatial-resolution will seriously deteriorate the effectiveness of the HSI in applications. This emphasizes the importance of resolution enhancement [16,17]
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