&lt;title&gt;Independent component analysis for remote sensing study&lt;/title&gt;

IEEE Trans. Geosci. Remote Sensing

2005

2,303

1,501

Abstract-Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: 1) the endmembers are the vertices of a simplex and 2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.Index Terms-Linear unmixing, simplex, spectral mixture model, unmixing hypespectral data, unsupervised endmember extraction, vertex component analysis (VCA).

Section: Evaluation Of the Vca Algorithmmentioning

confidence: 99%

Vertex component analysis: a fast algorithm to unmix hyperspectral data

IEEE Trans. Geosci. Remote Sensing

2005

2,303

1,501

“…In fact, the application of ICA to hyperspectral data has been proposed in [29], where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in [24], [30]- [36], where sources are the abundance fractions of each endmember. However, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances.…”

Section: Introductionmentioning

confidence: 99%

Does Independent Component Analysis Play a~Role in Unmixing Hyperspectral Data?

Pattern Recognition and Image Analysis

2003

114

Abstract-Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2) sources are statistically independent. Independent factor analysis (IFA) extends ICA to linear mixtures of independent sources immersed in noise. Concerning hyperspectral data, the first assumption is valid whenever the multiple scattering among the distinct constituent substances (endmembers) is negligible, and the surface is partitioned according to the fractional abundances. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be statistically independent, this compromising the performance of ICA/IFA algorithms in hyperspectral unmixing. This paper studies the impact of hyperspectral source statistical dependence on ICA and IFA performances. We conclude that the accuracy of these methods tends to improve with the increase of the signature variability, of the number of endmembers, and of the signal-to-noise ratio. In any case, there are always endmembers incorrectly unmixed. We arrive to this conclusion by minimizing the mutual information of simulated and real hyperspectral mixtures. The computation of mutual information is based on fitting mixtures of Gaussians to the observed data. A method to sort ICA and IFA estimates in terms of the likelihood of being correctly unmixed is proposed.Index Terms-Independent component analysis (ICA), independent factor analysis (IFA), mixture of Gaussians, unmixing hyperspectral data.

“…Independent Component Analysis (ICA) have also recently been proposed as a tool to unmix hyperspectral data [22], [23], [24], [25]. ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying dependence among abundances.…”

Section: Introductionmentioning

confidence: 99%

Vertex Component Analysis: A~Fast Algorithm to Extract Endmembers Spectra from Hyperspectral Data

Pattern Recognition and Image Analysis

2003

Abstract. Linear spectral mixture analysis, or linear unmixing, has proven to be a useful tool in hyperspectral remote sensing applications. It aims at estimating the number of reference substances, also called endmembers, their spectral signature and abundance fractions, using only the observed data (mixed pixels). This paper presents new method that performs unsupervised endmember extraction from hyperspectral data. The algorithm exploits a simple geometric fact: endmembers are vertices of a simplex. The algorithm complexity, measured in floating points operations, is O(n), where n is the sample size. The effectiveness of the proposed scheme is illustrated using simulated data.