Algorithms for clustering data
Algorithms for clustering data
Intrinsic Dimensionality Estimation With Optimally Topology Preserving Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Dependent Component Analysis: A Hyperspectral Unmixing Algorithm
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Anomaly preserving l2,∞-optimal dimensionality reduction over a Grassmann manifold
IEEE Transactions on Signal Processing
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Hyperspectral applications in remote sensing are often focused on determining the so-called spectral signatures, i.e., the reflectances of materials present in the scene (endmembers) and the corresponding abundance fractions at each pixel in a spatial area of interest. The determination of the number of endmembers in a scene without any prior knowledge is crucial to the success of hyperspectral image analysis. This paper proposes a new mean squared error approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense.