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A framework based on the blind source extraction (BSE) algorithm is proposed to detect targets in remotely sensed hyperspectral images. The mean square cross prediction error (MSCPE)-based BSE method is used as the kernel algorithm where the autoregressive (AR) parameters of the targets’ spectra are utilized as priors. Numerical simulations show that the proposed framework highlights the desired signal,...
In recent years, there has been a growing interest in the study of sparse representation. Sparse representation has been adopted in this paper for automatic target detection in hyperspectral image(HSI). We using a novel algorithm, the K-SVD, designing overcomplete dictionaries for sparse representation. The sparse representation is recovered by solving a greedy algorithm called Orthogonal Matching...
Feature extraction is an necessary preprocessing step for information extraction from hyperspectral remote sensing data. We exploit locality preserving projections (LPP) for dimensionality reduction, leading to a low-dimensional embedding. Given a subset of hyperspectral image, by LPP, we project them into a low-dimensional space to preserve much of the geometric structure. Hyperspectral image classification...
ASTER has higher spatial resolution in the VNIR region and higher spectral resolution in the shortwave infrared (SWIR) region in comparison with ETM+, which provide us an opportunity to distinguish various clay-alteration minerals accurately. In this paper, the 14 bands of ASTER were adopted to identify the lithologic and hydrothermal alteration minerals, such as the calcite, kaolinite and OH bearing...
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