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For hyperspectral image, low spatial and spectral resolution will cause inaccurate object detection and classification. In this paper, a novel jointly spatial-spectral resolution enhancement algorithm is proposed to promote the spatial resolution and spectral resolution simultaneously, high spatial resolution information from panchromatic image is used as constraint in spectral enhancement while the...
Hyperspectral image denoising and unmixing are two separate stages in traditional works. Unmixing algorithm is implemented after denoising. The performance of unmixing will be promoted if noise in hyperspectral image is removed well. But the result of unmixing can not be used to improve the result of denoising. In this paper we propose a joint denoising and unmixing algorithm for hyperspectral image...
Low spatial resolution is the mainly drawback of hyperspectral imaging. Image super-resolution techniques can be applied to overcome the limits. This paper presents a new framework for improving the spatial resolution of hyperspectral images base by combing high-resolution spectral information and high-resolution spatial information by image fusion and compressed sensing. Based on the compressed sensing...
For the instrument limitation and imperfect imaging optics, it is difficult to acquire high spatial resolution hyperspectral imagery. Low spatial resolution will result in a lot of mixed pixels and greatly degrade the detection and recognition performance, affect the related application in civil and military fields. As a powerful statistical image modeling technique, sparse representation can be utilized...
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