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This paper proposes a blind hyperspectral reconstruction technique termed spectral compressive acquisition (SpeCA) conceived to spaceborne sensors systems which are characterized by scarce onboard computing and storage resources and by communication links with reduced bandwidth. SpeCA exploits the fact that hyperspectral vectors often belong to a low-dimensional subspace and it is blind in the sense...
This paper introduces a new simplex-based unsupervised endmember extraction method from hyperspectral data. The method exploits the dimensionality reduction ability of the principal component analysis, and generalizes the concept that, the first generated endmember by the Simplex Growing Algorithm, is always a pixel which has either a maximum or a minimum value in the first component, to more endmembers...
In this paper, we investigate the use of random-projection-based dimensionality reduction for hyperspectral endmember extraction. It is data-independent and computationally more efficient than other widely used dimensionality reduction methods, such as principal component analysis and maximum noise fraction transform. Based on the preliminary result, random-projection-based dimensionality reduction...
In this paper, we investigate the use of the watershed transformation for integrating spatial and spectral information in the process of endmember extraction for spectral unmixing of hyperspectral images. The proposed approach is presented as a preprocessing module designed to automatically select a small subset of pixels containing potentially relevant candidates from both spatial and spectral point...
Several available techniques for endmember extraction and spectral unmixing use only the spectral information contained in the hyperspectral data. In this paper, we introduce a novel method for spatial-spectral endmember extraction which incorporates texture features in the quantification of spatial information (jointly with spectral information). Experimental results with simulated and real hyperspectral...
Random Projection (RP) has been proven to be a powerful technique for Dimension Reduction (DR). In this paper, it is applied to hyperspectral images as a DR preprocess step for Endmember Extraction (EE). Theoretical analysis demonstrates that RP can preserve geometric simplex fitting by hyperspectral data perfectly. Therefore, endmembers, which play an extremely important role for Spectral Mixture...
The target detection algorithm based on Independent Component Analysis (ICA) was proposed. The orthogonal subspace projection operator was used to extract the target endmembers and the initialization mixing matrix of the FastõICA was made up of such endmember vectors. This method could solve the ordering randomicity of independent vectors. In this paper, the Noise-Adjusted Principal Component Analysis...
In this paper, a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery is introduced. Following the linear mixing model, each pixel spectrum of the hyperspectral image is decomposed as a linear combination of pure endmember spectra. The estimation of the unknown endmember spectra and the corresponding abundances is conducted in a unified manner by generating...
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