Abstract
Due to sensor limitations, hyperspectral images (HSIs) are acquired by hyperspectral sensors with high-spectral-resolution but low-spatial-resolution. It is difficult for sensors to acquire images with high-spatial-resolution and high-spectral-resolution simultaneously. Hyperspectral image super-resolution tries to enhance the spatial resolution of HSI by software techniques. In recent years, various methods have been proposed to fuse HSI and multispectral image (MSI) from an unmixing or a spectral dictionary perspective. However, these methods extract the spectral information from each image individually, and therefore ignore the cross-correlation between the observed HSI and MSI. It is difficult to achieve high-spatial-resolution while preserving the spatial-spectral consistency between low-resolution HSI and high-resolution HSI. In this paper, a self-dictionary regression based method is proposed to utilize cross-correlation between the observed HSI and MSI. Both the observed low-resolution HSI and MSI are simultaneously considered to estimate the endmember dictionary and the abundance code. To preserve the spectral consistency, the endmember dictionary is extracted by performing a common sparse basis selection on the concatenation of observed HSI and MSI. Then, a consistent constraint is exploited to ensure the spatial consistency between the abundance code of low-resolution HSI and the abundance code of high-resolution HSI. Extensive experiments on three datasets demonstrate that the proposed method outperforms the state-of-the-art methods.
Highlights
With the developments of hyperspectral sensors, hyperspectral images (HSIs) have been widely used in numerous applications [1,2,3], such as remote sensing classification [4,5,6], change detection [7] and target detection [8]
When the number of consistency samples is equal to the number of pixels in the low-resolution HSI n, we achieve the best performance on root mean square error (RMSE) and mean peak signal-to-noise ratio (MPSNR)
We propose a self-dictionary sparse regression to enhance the spatial-resolution of HSI
Summary
With the developments of hyperspectral sensors, hyperspectral images (HSIs) have been widely used in numerous applications [1,2,3], such as remote sensing classification [4,5,6], change detection [7] and target detection [8]. The low-spatial-resolution in HSI will result in mixed pixels and greatly degrade the further processing in the remote sensing applications [9,10]. Enhancing the spatial-resolution of HSI has become an important issue in the remote sensing community [11,12,13]. HSI super-resolution is supposed as an inverse problem [11,16,17]: the original high-spatial-resolution HSI can be recovered from the low-resolution observations [14,18,19]. The missing spatial information in low-resolution HSI can be compensated by utilizing the prior knowledge in a high-resolution coincident image of the same scene [20], such as panchromatic image (PAN), RGB image and multispectral image (MSI)
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