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Hughes phenomenon is a serious problem in supervised classification of hyperspectral images in particular for small land-cover patches. A solution for this problem through integrating the capabilities of a nonlinear discriminating analysis with relevance vector machine (RVM) is proposed in this paper. It first transforms the hyperdimensional data to a new space with a better class separability. Then,...
In land-cover analysis of hyperspectral remotely sensed images through supervised classification methods, a frequent problem is that the available training samples corresponding to different land-covers are not sufficient. This problem is especially severe for small land-covers and targets which often include the key information of the scene. Furthermore, degrading due to “boundary effect” is more...
In this paper an improved supervised classification technique through applying a superresolution method on remotely sensed hyperspectral images is introduced. Superresolution methods provide high-resolution images from a sequence of low-resolution frames. In the proposed technique, classification of the hyperspectral image is carried out using spectrally homogenous training classes of pixels. Low...
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