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Hyperspectral image super resolution (SR) reconstruction has been studied widely and many algorithms have been proposed. In this paper, a novel super resolution reconstruction method was designed by employing a joint spectral-spatial sub-pixel mapping model which aims to obtain the probabilities of sub-pixels to belong to different land cover classes by dividing mixed pixels into several sub-pixels...
Hyperspectral images (HSIs) provide abundant information to solve various kinds of problems like object identification and classification. However, HSIs often inevitably suffer many factors from various resources [1], such as imperfect imaging optics, sensor noise, and atmospheric effects, which degrade the acquired image quality [2]. Thus, HSI image super resolution reconstruction, used to achieve...
The sub-pixel mapping technique, which can provide a fine-resolution map of class labels, has attracted more and more attention in recent years. Generally speaking, there are two kinds of methods used to realize the sub-pixel labeling. The first kind are image reconstruction based methods, which first improve the spatial resolution of an image by the super-resolution technique, and then perform a...
Mixed pixel is a common problem in Remotely Sensed classification. Even though the composition of these pixels for different classes can be estimated with pixel un-mixing model, the output provides no indication of how such classes are distributed spatially within these pixels. Sub-pixel mapping is a technique designed to obtain the spatial distribution of these classes in these pixels with information...
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