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Deep learning techniques have brought in revolutionary achievements for feature learning of images. In this paper, a novel structure of 3-Dimensional Convolutional AutoEncoder (3D-CAE) is proposed for hyperspectral spatial-spectral feature learning, in which the spatial context is considered by constructing a 3-Dimensional input using pixels in a spatial neighborhood. All the parameters involved in...
The rich spectral information in hyperspectral imagery gives rise to huge storage and transmission costs. Dimensionality reduction aims to reduce the space complexity in hyperspectral imagery by projecting data into a low-dimensional subspace. There has been an increasing interest in dimensionality reduction driven by random projections due to its data-independent representation as well as desirable...
By coupling the nearest-subspace classification with a distance-weighted Tikhonov regularization, nearest regularized subspace (NRS) was recently developed for hyperspectral image classification. However, the NRS was originally designed to be a pixel-wise classifier which considers the spectral signature only while ignoring the spatial information at neighboring locations. Gabor features have currently...
Multilinear principal component analysis (MPCA) is explored for hyperspectral imagery classification in this study. MPCA is first applied to the original image data, in 3rd-order tensor form, to reduce the dimensionality. Then, a multiscale transform technique (i.e., contourlet transform, etc.) is applied to each of the principal components (PCs) generated from MPCA. The resulting transform coefficients...
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